Opera Medica et Physiologica

Augmentation of Sensorimotor Functions with Neural Prostheses

Author Affiliations

Duke University, Durham, North Carolina, USA.

Corresponding author: 

Mikhail Lebedev ( lebedev@neuro.duke.edu)

Published ahead of print December 15, 2016; Printed December 25, 2016; OM&P 2016 Volume 2 Issue 3, 4, pages 211-227; doi:10.20388/ OMP.003.0035

Neural prostheses (NPs) link the brain to external devices, with an eventual goal of recovery of motor and sensory functions to patients with neurological conditions. Over the past half-century, NPs have advanced significantly from the early ideas that sounded like science fiction to the modern high-tech implementations. In particular, invasive recordings using multichannel implants have enabled real-time control of artificial limbs by nonhuman primates and human subjects. Furthermore, NPs can provide artificial sensory feedback, allowing users to perceive the movements of prosthetic limbs and their haptic interaction with external objects. Recently, NP approach was used to build brain-nets that enable information exchange between individual brains and execution of cooperative tasks. This review focuses on invasive NPs for sensorimotor functions.

History of Neural Prostheses

Many would agree that modern Neuroscience started with the pioneering discoveries of Ramon Cajal (Finger 1994, Ramón y Cajal 1995) and Camillo Golgi (Golgi 1995) on the structure and connectivity of brain neurons, recognized by the Nobel prize in 1906. These founding fathers of Neuroscience principally disagreed in their views on the morphology and function of individual neurons. Cajal described single neurons as morphological entities. Golgi argued that neurons are not separated anatomically and do not work individually. He insisted that they are fused into a network. Although Golgi’s views were initially rejected, they turned to be valid at the end, as ion-conducting gap junctions were discovered between many types of brain neurons (Lewis and Rinzel 2003, Connors and Long 2004), the complexity that makes it difficult to describe brain networks as circuits composed of individual neurons similarly to the way electrical circuits are composed of transistors. The discovery of ephaptic coupling (Anastassiou, Perin et al. 2011), that is coupling by electrical fields produced by neurons, adds more complexity to brain network operations.
While many reports can be found in the literature of very specific response properties of single neurons, such as grandmother cells (Gross 2002) and Jennifer Aniston cells (Quiroga, Reddy et al. 2005), there is also a sizeable literature claiming that information is represented in the brain by large populations of neurons located in multiple neural regions (Houk and Wise 1995, Nicolelis and Lebedev 2009).
In motor neurophysiology, the studies of Apostolos Georgopoulos have been particularly influential to advance the population encoding ideas (Georgopoulos, Schwartz et al. 1986, Georgopoulos, Kettner et al. 1988, Georgopoulos 1994). Georgopoulus proposed a population vector model to explain how neuronal ensembles represent motor parameters. He also assessed the size of neuronal population needed to produce an accurate representation. However, Georgopoulus did not record from many neurons simultaneously; his conclusions were based on the analyses of neural data collected in a serial manner over many days. With this approach, information could not be extracted from a sufficiently large neuronal ensemble in real time.
A decisive development was achieved in the 90s by Miguel Nicolelis, John Chapin and several other researchers who pioneered techniques for multielectrode recordings (Nicolelis, Lin et al. 1993, Wilson and McNaughton 1993, Nicolelis, Ghazanfar et al. 1998). These recording methods form the foundation of modern invasive NPs. 
NPs are artificial systems for bidirectional communication with the brain (Figure 1). The main goal of NPs is the development of medical applications, such as neurally controlled limb prostheses for paralyzed patients. Additionally, NP approach finds applications in such areas as computer gaming (Mason, Bohringer et al. 2004, Finke, Lenhardt et al. 2009, Martisius and Damasevicius 2016), safety systems that monitor drivers’ state of wakefulness (Picot, Charbonnier et al. 2008, Liu, Chiang et al. 2013, Garces Correa, Orosco et al. 2014) and even education (Marchesi and Riccò 2013). Some view NP as a technology for augmenting brain functions (Maguire and McGee 1999, Farah and Wolpe 2004, Madan 2014, Zehr 2015). NPs are interchangeably referred to as brain-machine interfaces (BMIs) (Lebedev, Crist et al. 2008, Sakurai 2014), brain-computer interfaces (BCIs) (Allison, Wolpaw et al. 2007, Serruya 2015) and even biohybrids (Zehr 2015, Vassanelli and Mahmud 2016). Since NP interfere with the mind, they bring about many ethical issues (Attiah and Farah 2014, Glannon 2014, Schleim 2014, Hildt 2015, Kyriazis 2015).
Historically, the venture into reading out the brain content started in the 60-70s when the idea of biofeedback gained popularity (Dahl 1962, Smith and Ansell 1965, Kamiya 1971, Sterman 1973, Kaplan 1975, Suter 1977). Biofeedback of neural activity is often called neurofeedback. Such neurofeedback, usually provided by visual or auditory signals, gives subjects an ability to monitor and volitionally modify their own brain activity. For example, subjects can learn to modify their electroencephalographic (EEG) rhythms (Evans and Abarbanel 1999). 
The first demonstration that can be described as an NP dates to 1963, when Gray Walter demonstrated neural control of an external device by human subjects. Although Walter himself did not publish these results one can learn about them from the writings of Daniel Dennett who attended Walter’s lecture (Dennett 1991). Per Dennett, Walter recorded from motor cortical activity in human patients using implanted electrodes. The patients were instructed to press a button to advance a slide projector, the task that Walter used to investigate motor cortical readiness potentials that developed several hundred milliseconds prior to movement onset. Next, Walter electrically disconnected the button and switched to using the readiness potential as the trigger to advance the slides. The subjects were surprised that in this mode of operation the machinery detected their motor intentions before they initiated the hand movements. 
The goal of building NPs was explicitly formulated in the late 60s by Karl Frank, the chief of the laboratory of neural control at National Institutes of Health (NIH). He stated that the  laboratory would be developing systems that link the brain to external devices and computers (Frank 1968). Frank was also involved in collaborative research on NPs that restore vision to the blind. The NIH laboratory conducted their experiments in monkeys. They simultaneously recorded from 3-8 neurons in the motor cortex while monkeys flexed or extended their wrists. In offline analyses, the parameters of wrist movements were reconstructed from the neuronal data using multiple linear regression (Humphrey, Schmidt et al. 1970). This work continued for a decade and culminated in a demonstration of real-time decoding of cortical signals (Schmidt 1980): recordings were conducted using 12 electrodes chronically implanted in the motor cortex, and the implanted monkey learned to control one-dimensional movements of a cursor on an LED display with its cortical activity.
In the late 60s, Eberhard Fetz trained monkeys to volitionally control the activity of single neurons in their motor cortex (Fetz 1969). Fetz interpreted these findings in terms of neurofeedback. In these experiments, monkeys learned to modulate the discharge rate of their neurons, provided they had a visual or auditory indicator of those discharges. 
Around the same time, Michael Craggs used baboons with spinal cord transections to test the possibility of restoring motor function to paraplegic subjects (Craggs 1975). Prior to the spinal cord injury, the baboons were trained on a leg movement task Craggs discovered that, even after the motor cortical representation of the leg was disconnected from the spinal cord, it continued to generate task-related activity recorded with epidural electrodes. Accordingly, Craggs suggested that this type of recordings could be used for functional recovery of patients with leg paralysis.
While these scientists developed NPs for extraction of motor commands from the brain, other researcher groups started developing NPs for the delivery of sensory information to the brain. They electrically stimulated peripheral sensory nerves (Collins, Nulsen et al. 1960, Hensel and Boman 1960) and sensory areas of the brain (Libet, Alberts et al. 1964, Brindley and Lewin 1968, Brindley and Lewin 1968) to evoke artificial, but still recognizable sensations. Among these developments, a cochlear implant has achieved a spectacular success, with hundreds of thousands deaf people implanted with this device (Djourno and Eyriès 1957, Simmons, Mongeon et al. 1964, House 1976, Wilson and Dorman 2008). Some progress has been made in the development of visual NPs: the groups led by Giles Brindley(Brindley and Lewin 1968, Brindley and Lewin 1968, Brindley 1970) and William Dobell (Dobelle and Mladejovsky 1974, Dobelle, Mladejovsky et al. 1974, Dobelle, Mladejovsky et al. 1976, Dobelle, Quest et al. 1979) electrically stimulated visual cortex in blind patients. The stimulation evoked perceptions of light flashes, called phosphenes, and combinations of phosphenes produced by multi-channel stimulation could be matched to visual objects. The studies on visual NPs continue nowadays (Christie, Ashmont et al. 2016, Lewis, Ayton et al. 2016). There is also ongoing work on vestibular NPs (Shkel and Zeng 2006, Golub, Phillips et al. 2011).
As noted above, a significant breakthrough in the development of NPs occurred in the mid-90s, with the advent of chronic multielectrode implants (Buzsaki, Bickford et al. 1989, Nicolelis, Lin et al. 1993, Nicolelis, Lin et al. 1993, Nicolelis, Baccala et al. 1995). In 1999, Chapin, Nicolelis and their colleagues published a landmark study, where rats learned to control a simple robotic manipulator with their cortical ensembles (Chapin, Moxon et al. 1999). Following this success, Nicolelis commenced a series of NP studies in monkeys, including both New World and Old World species. A landmark study published in 2000, where owl monkeys controlled movements of a robotic arm with their cortical activity (Wessberg, Stambaugh et al. 2000). This work led to a series of monkey studies on NPs enabling arm movements (Serruya, Hatsopoulos et al. 2002, Taylor, Tillery et al. 2002, Carmena, Lebedev et al. 2003). 
Invasive NP research has been also conducted in humans. The multielectrode implant, called the Utah probe, is approved for human trials. This is a silicon-based matrix of needle electrodes in a 10x10 arrangement (Campbell, Jones et al. 1991, Nordhausen, Maynard et al. 1996, Maynard, Nordhausen et al. 1997). The other electrode approved for human recordings is the neurotrophic electrode developed by Philip Kennedy (Kennedy 1989, Kennedy, Bakay et al. 1992, Kennedy, Mirra et al. 1992). The electrode contains nerve growth factor that promotes neurite growth into the glass cone to which recording microwires are connected. Kennedy and his colleagues reported that this recording method allowed severely paralyzed patients to operate several types of NPs that restored their communication with the outside world (Guenther, Brumberg et al. 2009, Brumberg, Nieto-Castanon et al. 2010).
Noninvasive NPs, i.e. the devices with recording sensors that do not penetrate the body, experienced their own impressive development. These systems are not described in detail here. In brief, Jacques Vidal pioneered this research the 70s by decoding EEG evoked responses (Vidal 1973). In 1988, the first report was published where human subjects controlled a robot with their EEG (Bozinovski, Sestakov et al. 1988). In that study, subjects issued binary commands by closing and opening their eyes. This maneuver started and stoped an alpha recorded with EEG sensors placed over the occipital cortex.  These studies towards the development of an NP for disabled patients culminated in the publication by Niels Birbaumer of a pivotal study on an EEG-based spelling device for locked-in patients (Birbaumer, Ghanayim et al. 1999). The device utilized slow cortical potentials. 


Figure 1. Schematics of a neural prosthesis (NP) controlled by a rhesus monkey. A monkey is implanted with multielectrode arrays placed in multiple cortical areas. These cortical implants are used for both recording of neuronal ensemble activity and stimulating cortical tissue with electrical pulses. The monkey is seated in front of a computer screen that displays a virtual hand and several targets. The monkey explores the targets with the virtual hand. This exploration is performed either manually, using a joystick, or through the NP. In the NP mode of operation, cortical ensemble activity is processed by a decoder to generate kinematic parameters of the virtual arm. Each time the virtual arm touches a screen target, a pattern of electrical stimulation is applied to the somatosensory cortex. The stimulation mimics artificial textures of the targets. Thus, this NP enables bidirectional communication with the brain: motor commands are extracted from cortical activity simultaneously with the delivery of somatosensory feedback back to the brain. Aided with this NP, the monkey actively explores the targets, finds the one associated with a particular artificial texture, and receives a reward for the correct identification of that texture.

Figure 1. Schematics of a neural prosthesis (NP) controlled by a rhesus monkey. A monkey is implanted with multielectrode arrays placed in multiple cortical areas. These cortical implants are used for both recording of neuronal ensemble activity and stimulating cortical tissue with electrical pulses. The monkey is seated in front of a computer screen that displays a virtual hand and several targets. The monkey explores the targets with the virtual hand. This exploration is performed either manually, using a joystick, or through the NP. In the NP mode of operation, cortical ensemble activity is processed by a decoder to generate kinematic parameters of the virtual arm. Each time the virtual arm touches a screen target, a pattern of electrical stimulation is applied to the somatosensory cortex. The stimulation mimics artificial textures of the targets. Thus, this NP enables bidirectional communication with the brain: motor commands are extracted from cortical activity simultaneously with the delivery of somatosensory feedback back to the brain. Aided with this NP, the monkey actively explores the targets, finds the one associated with a particular artificial texture, and receives a reward for the correct identification of that texture.

Types of Neural Prostheses 

Several classifications have been proposed to describe different types of NPs. NPs can be classified by function into: (1) motor NPs, (2) sensory NPs, (3) sensorimotor NPs, (4) cognitive NPs, and (5) brain-nets. Motor NPs generate movements, for example movements of artificial limbs (Wessberg, Stambaugh et al. 2000, Carmena, Lebedev et al. 2003, Velliste, Perel et al. 2008, Collinger, Wodlinger et al. 2013) or movements of a motorized wheelchair (Xu, So et al. 2014, Rajangam, Tseng et al. 2016). Sensory NPs evoke sensations using electrical (Romo, Hernández et al. 1998) or optogenetic (Jarvis and Schultz 2015, Kwon, Lee et al. 2015) stimulation of nervous tissue. Sensorimotor NPs, also called bidirectional, simultaneously produce movements and evoke sensations (O'Doherty, Lebedev et al. 2009, O'Doherty, Lebedev et al. 2011, Bensmaia and Miller 2014). Cognitive NPs (Andersen, Burdick et al. 2004, Andersen, Hwang et al. 2010) reproduce higher-order functions, notably  attention (Astrand, Wardak et al. 2014, Ordikhani-Seyedlar, Lebedev et al. 2016), memory (Berger, Hampson et al. 2011, Deadwyler, Hampson et al. 2013, Madan 2014, Song, Harway et al. 2014), and decision making (Musallam, Corneil et al. 2004). Brain-nets are NPs incorporating several interconnected brains (Ramakrishnan, Ifft et al. 2015). 
The other useful classification of NPs is the classification into invasive (Chapin, Moxon et al. 1999) and noninvasive (Birbaumer, Ghanayim et al. 1999, Waldert 2016) NPs. Invasive NPs provide better-quality neural recordings, but they carry risks to patients, including risk of tissue damage by invasive surgical procedures and electrode insertion, and the possibility of infection, particularly when recording cables pierce the skull and skin. Noninvasive NPs do not have such risks, but often suffer from low spatial and temporal resolution of the recorded neural signals.
NPs can be also classified by their operation principle into endogenous and exogenous devices. Endogenous NPs mimic “free will”:  users are free to choose the type and timing of actions. For example, in a motor imagery NP, users imagine moving their body parts to generate NP output (Obermaier, Neuper et al. 2001, Pfurtscheller and Neuper 2006). Exogenous NPs require an external stimulus to operate, and that stimulus paces the actions. The stimulus evokes a neuronal response, and the user task consists of volitionally controlling that response (Sellers, Krusienski et al. 2006, Lee, Sie et al. 2010). A popular exogenous NP utilizes P300 evoked potentials, which increase when the user attends to the stimulus (Donchin, Spencer et al. 2000, Finke, Lenhardt et al. 2009, Brunner, Ritaccio et al. 2011). 

Neural Representation of Information

Although we still have a rather poor understanding of how the brain represents and processes information, the term “neuronal encoding” is commonly used to describe the properties of neuronal discharges. Usually, what is meant by neuronal encoding is the correlation of neuronal discharge rate to a behavioral parameter or an external stimulus. For example, discharge rates of neurons in motor areas clearly correlate with limb kinematics, and the rates of neurons in visual areas correlate with the features of visual stimuli. Such a correlation is often referred to as “neuronal tuning”. 
Neurons tuned to a certain behavioral parameter could be used by a BMI designed to extract the same parameter. In neurophysiology, such neurons are called task-related neurons. Even the best task-related neurons represent behavioral parameters in a noisy way, which hinders BMI decoding. Decoding accuracy can be improved by extracting information from many neurons simultaneously (Fetz 1992, Nicolelis and Lebedev 2009, Lebedev 2014). Combining contributions from many neurons improves the signal to noise ratio because noisy inputs from different neurons cancel each other, unless this is a common noise.
The pioneering work on neuronal tuning was conducted by Edward Evarts who developed the technique of single-unit recordings from the brain of awake, behaving monkeys (Evarts, Bental et al. 1962, Evarts 1964, Evarts 1966). Evarts usually recorded from one neuron at a time using a sharp-tipped electrode that he inserted in monkey motor cortex. Monkey were trained to perform motor tasks. Evarts found that neuronal discharge rates reflected movement onsets and the force of muscle contraction.
Apostolos Georgopoulos used Evarts’ recording methods to explore the relationship between the discharges of motor cortical neurons and the direction of arm movement. He described that relationship as a broad tuning curve that could be fitted with a cosine function of movement direction angle (Georgopoulos, Schwartz et al. 1986, Georgopoulos, Kettner et al. 1988, Kettner, Schwartz et al. 1988, Schwartz, Kettner et al. 1988). 
With the advent of multielectrode recordings, investigators started to get more insights on the neuronal population encoding. It was experimentally that decoding accuracy improves with the neuronal population size (Blazquez, Fujii et al. 2002, Musallam, Corneil et al. 2004, Batista, Yu et al. 2008, Lebedev 2014, Montijn, Vinck et al. 2014). It was also discovered that the physiology of neuronal populations is governed by certain principles (Nicolelis and Lebedev 2009). Among these principles, single neuron insufficiency principle explains that each individual neuron encodes only a small amount of information. Mass effect principle states that a certain number of neurons (a mass) is required for the represented amount of information to stabilize. After the neuronal mass is reached, adding more neurons changes the information content very little, and many more neurons needed to be recorded for extracting new information. Distributed encoding principle asserts that many brain areas encode and process the same information; there is no localized processing in the brain. Multiplexing principle describes the property of individual neurons to represent many variables simultaneously. The conservation of firing principle states that the average discharge frequency of the neurons in the ensemble remains approximately constant, even when the brain state changes. A somewhat similar principle is called free energy principle (Friston, Kilner et al. 2006, Friston 2009, Friston 2010, Tozzi, Zare et al. 2016). The context principle states that ensemble activity patterns critically depend on the behavioral context: neuronal responses to the same stimulus could differ dramatically in different contexts. And, finally, the plasticity principle highlights the capacity of neuronal ensembles to adapt to new conditions and behaviors.

Recording Methods

Currently, microwire implants are the most popular method of neuronal ensemble recordings (Nicolelis, Ghazanfar et al. 1997, Kralik, Dimitrov et al. 2001, Schwarz, Lebedev et al. 2014). Microwires in such an implant can be individually movable or fixed. This method is suitable for recording neuronal activity in both the cortex and subcortical areas. The Utah array composed of silicon electrodes is another popular recording method (Campbell, Jones et al. 1991). As mentioned above, the Utah array has been approved for human trials.
New recording methods are constantly being developed. The main goals here are increasing the number of recording channels, minimizing tissue damage and increasing recording longevity. These goals are achieved using novel floating (Gualtierotti and Bailey 1968, Musallam, Bak et al. 2007, Neves, Orban et al. 2007, Spieth, Brett et al. 2011) and flexible (Takeuchi, Suzuki et al. 2003, Kozai and Kipke 2009, Hassler, Guy et al. 2011, Agorelius, Tsanakalis et al. 2015, Agorelius, Tsanakalis et al. 2015) implants. One promising method, called sinusoidal probe, uses thin, flexible electrodes with reduced motion relative to the nervous tissue (Sohal, Jackson et al. 2014).
Several microelectrodes designs improve the yield and quality of neuronal recordings. NeuroNexus microelectrodes increase the number of recording channels by having multiple contacts along the shaft (Najafi, Wise et al. 1985, Anderson, Najafi et al. 1989, Weiland and Anderson 2000, Vetter, Williams et al. 2004). Twisted bundles of four electrodes, called tetrodes, have enhanced capacity to discriminate single units (Recce and O’keefe 1989, Wilson and McNaughton 1993, Jog, Connolly et al. 2002, Santos, Opris et al. 2012).
Recently, a principally new recording method has been introduced, called neural dust (Seo, Carmena et al. 2015, Seo, Neely et al. 2016). Neural dust is composed of small (10-100 microns) sensors that detect bioelectrical potentials. The sensors communicate with an external transducer though an ultrasonic link. Each sensor has a piezoelectric element that reflects the ultrasound sent from the transducer, and the reflected signal changes depending on the electrical potential detected by the sensor.
Another electrode type, called endovascular electrode, penetrates the brain through the blood vessels. Endovascular nano-electrodes can penetrate into the brain capillaries without breaking the blood-brain barrier (Llinas, Walton et al. 2005). Additionally, larger endovascular electrodes can be placed into cerebral arteries to record neural signals similar to EEG recordings (Boniface and Antoun 1997). A multichannel endovascular probe, called stentrode, was recently developed (Oxley, Opie et al. 2016). The stentrode was introduced into the sheep brain and retained good recording quality for 190 days.
Optical recordings are another method that can be employed for sampling signals from neuronal populations. These methods utilize fluorescent markers that are sensitive to voltage (Tasaki, Watanabe et al. 1968, Patrick, Valeur et al. 1971, Grinvald, Frostig et al. 1988, Grinvald and Hildesheim 2004) or intracellular calcium (Smetters, Majewska et al. 1999, Stosiek, Garaschuk et al. 2003, Grewe, Langer et al. 2010).
Electrocorticographic (ECoG) recordings represent a minimally invasive method for recording cortical activity (Crone, Sinai et al. 2006, Leuthardt, Miller et al. 2006, Miller, Shenoy et al. 2007, Hill, Gupta et al. 2012). High-density ECoG grids offer a significantly improved resolution of recordings (Wang, Degenhart et al. 2009, Viventi, Kim et al. 2011, Bleichner, Freudenburg et al. 2016). 
Multichannel neuronal recordings usually require cables to connect the electrodes to external recording equipment (Chapin, Moxon et al. 1999, Wessberg, Stambaugh et al. 2000, Serruya, Hatsopoulos et al. 2002, Taylor, Tillery et al. 2002, Carmena, Lebedev et al. 2003). More recently, a variety of wireless recording methods has been developed (Obeid, Nicolelis et al. 2004, Morizio, Irazoqui et al. 2005, Borghi, Bonfanti et al. 2007, Chestek, Gilja et al. 2009, Harrison, Kier et al. 2009, Kim, Bhandari et al. 2009, Zippo, Romanelli et al. 2015).

Decoding Algorithms

Mathematical algorithms for decoding information from neuronal activity can be generally described as multiple-input, multiple-output (MIMO) models (Kim, Sanchez et al. 2006). The characteristics of a decoder are often set up using a training recording session. During that session, subjects either perform overt limb movements or passively the movements of an external actuator while imagining that they control those movements. The training session is needed to measure the relationship between the neuronal discharges and the behavioral parameters of interests. For example, if a subject moves the arm in different directions for some time, the decoder could be trained to extract arm kinematics from the neuronal activity. After the decoder is trained, the mode of operation can be switched to brain control, during which the decoder output controls an external device. Additionally, adaptive decoders can be used to adjust an ongoing brain control (Taylor, Tillery et al. 2002, Carmena, Lebedev et al. 2003, Ganguly and Carmena 2009, Li, O'Doherty et al. 2011, Orsborn, Dangi et al. 2012, Dangi, Gowda et al. 2014).
A great variety of neural decoders have been developed over the years (Schwartz, Taylor et al. 2001, Li 2014, Agorelius, Tsanakalis et al. 2015). The simplest, but also very effective algorithm is the linear model that represents output signals as weighted sums of neuronal firing rates (Wessberg, Stambaugh et al. 2000, Taylor, Tillery et al. 2002, Carmena, Lebedev et al. 2003, Wessberg and Nicolelis 2004) (Figure 2). For example, Georgopoulos’ population vector algorithm is a linear model that computes a vector some of unit vectors pointing in the neurons’ preferred directions and weighted by the neuronal frequencies of discharge (Georgopoulos, Schwartz et al. 1986, Georgopoulos, Kettner et al. 1988, Georgopoulos, Lurito et al. 1989). The population vector algorithm, however, is not optimal because it does not minimize the decoding error. A better method, the Wiener filter (Figure 2), is an optimal linear algorithm that minimizes the error using a well-known multiple linear regression approach (Haykin 2014). To calculate a parameter of interest at time t, the Wiener filter measures neuronal rates at multiple time points preceding t, called taps, lags or bins, and assigns separate weight for each neuron, and for each tap.
Kalman Filter (Kalman 1960, Kalman and Bucy 1961) is another popular decoding algorithm that has been employed in many NPs (Serruya, Shaikhouni et al. 2003, Patil, Carmena et al. 2004, Wu, Shaikhouni et al. 2004, Kim, Sanchez et al. 2006, Li, O'Doherty et al. 2009, Okorokova, Lebedev et al. 2015). The filter separates variables into the state variables (for example, arm position and velocity) and observed variables (neuronal discharge rates). The relationship between the state variables and neuronal rates is described by function called the tuning model, and the dynamical properties of the state variables are described by the state model. The Kalman filter uses both models to update the state variables data in discrete steps, for example every 50-100 ms. Each update consists of several steps. First, an estimation of new state is generated from the previous state using the state model. Next, an expectation of neuronal rates is derived from the estimated state and the tuning model. That expectation is compared with the observed neuronal discharges, and, based on this comparison, the state estimation is corrected. An improved filter, the unscented Kalman filter, accounts for nonlinear relationship between neuronal rates and state variables, and outperforms the classical Kalman filter (Li, O'Doherty et al. 2009). It has been suggested that the brain itself uses computations like the Kalman filter (Wolpert and Ghahramani 2000).
Several adaptive decoders have been developed to improve stability of decoding. One such algorithm let monkeys to control a virtual arm with their cortical activity for 29 days without the need to run training sessions (Li, O'Doherty et al. 2011). An alternative approach is to fix the decoder settings and allow the nervous systems to plastically adapt to improve the performance (Ganguly and Carmena 2009).
Decoders have been recently introduced that track the distance between the cursor and target of movement, and adjust their parameters to minimize that distance (Kowalski, He et al. 2013, Suminski, Fagg et al. 2013, Shanechi, Orsborn et al. 2014).
Reinforcement learning is yet another adaptive algorithm employed in NPs (DiGiovanna, Mahmoudi et al. 2009). This algorithm updates its parameters based on the success or errors of the behavioral trials. In one implementation of reinforcement learning, an error signal was extracted from the brain itself, namely from the activity of nucleus accumbens, making this NP a self-sufficient, unsupervised learning system (Mahmoudi, Pohlmeyer et al. 2013). 

Figure 2. Decoding neuronal ensemble activity using a linear decoder (Wiener filter). For a time of interest, t, neuronal firing rates are measured in a time window preceding t. The window is split into several bins, also called taps; firing rates are measured within each bin. A behavioral parameter of interest (for example, arm coordinate) is then represented as a weighted sum of neuronal rates for different bins. The weights are calculated using the well-known multiple linear regression methods.

Figure 2. Decoding neuronal ensemble activity using a linear decoder (Wiener filter). For a time of interest, t, neuronal firing rates are measured in a time window preceding t. The window is split into several bins, also called taps; firing rates are measured within each bin. A behavioral parameter of interest (for example, arm coordinate) is then represented as a weighted sum of neuronal rates for different bins. The weights are calculated using the well-known multiple linear regression methods.


Neurally Controlled Prosthetic Arms

The development of neurally controlled prostheses of the upper limbs has been one the main directions of NP research (Carmena, Lebedev et al. 2003, Hochberg, Bacher et al. 2012, Collinger, Wodlinger et al. 2013). Such interest to the upper limb function is understandable because of the role that arm movements have in our motor repertoire. The first demonstration of the control of a robotic arm by a primate was performed using owl monkeys as an experimental model (Wessberg, Stambaugh et al. 2000). That was an open-loop brain control because monkeys did not receive any sensory feedback from the robot (Wessberg, Stambaugh et al. 2000). While the monkeys performed a reaching task with a joystick, activity of their motor cortical neuronal ensembles was recorded, decoded with a Wiener filter and sent to the robot. The robot reproduced the joystick movements with some errors, which the monkeys obviously could not correct.
Brain control in a closed-loop mode (i.e. with a vision of the robot or cursor, or other type of sensory feedback) was first demonstrated in rhesus monkeys (Serruya, Hatsopoulos et al. 2002, Taylor, Tillery et al. 2002, Carmena, Lebedev et al. 2003). Jose Carmena and his colleagues chronically implanted rhesus monkeys with mulielectrode arrays in multiple cortical areas, and trained the animals to control the movements of a robotic arm with the recorded cortical activity (Carmena, Lebedev et al. 2003). The robotic arm performed reaching and grasping movements. Monkeys started with controlling the robot arm manually, using a two-dimensional joystick that could be also squeezed to generate grip force of the robot. The monkeys did not have vision of the robot, but received visual feedback from it on a computer screen. The robot position was indicated by a computer cursor, and the grip force was indicated by the cursor size. Reach targets showed up on the screen, as well. While the monkeys performed the task manually, three Wiener filters were trained to extract X and Y components of joystick velocity and the grip force from cortical ensemble activity. Next, the joystick was electrically disconnected from the robot, and the Wiener filters’ outputs controlled the robot instead. The monkeys continued to assist themselves by moving the joystick for some time during this brain control mode. The joystick was then physically removed from the setup, after which the performance accuracy initially dropped but then improved.
A somewhat similar experiment was conducted by Dawn Taylor and her colleagues at the laboratory of Andrew Schwartz (Taylor, Tillery et al. 2002). In that study, monkey wore stereoscopic glasses that displayed a cursor in a three-dimensional space. The cursor position was controlled by the motor cortical activity using a population vector decoder. A coadaptive algorithm was employed to improve the decoding. The coadaptation consisted of comparing the cursor trajectories to the ideal trajectories connecting the starting position with the target, and adjusting the population vector weights to bring the trajectories closer to the ideal ones. In the next study of the Schwartz laboratory, monkeys controlled a robotic hand that grasped pieces of food and brought them to the monkey’s mouth (Velliste, Perel et al. 2008). 
John Donoghue and his colleagues demonstrated real-time cortical control of a computer cursor and robotic hand in human patients (Hochberg, Serruya et al. 2006). Paralyzed human subjects received Utah probes in the motor cortex, which allowed to record several tens single-units. Several years later the same group demonstrated a neuroprosthetic arm that picked up a coffee bottle and brought it to patient’s mouth (Hochberg, Bacher et al. 2012). In that experiment, the performance was assisted by shared control, where some operations were handled by a robotic controller instead of the patient. The neural part of the control was handled by the Kalman filter.
Recently, Andrew Schwartz and his colleagues recorded several hundreds of single units in the human motor cortex (Collinger, Wodlinger et al. 2013). With this improved recording quality, patients learned to control a seven degrees of freedom robotic arm that reached toward knobs, grasped them, and turned in different directions.
Peter Ifft and his colleagues reported a further achievement in NPs for arm control (Ifft, Shokur et al. 2013). In those experiments, monkeys controlled two virtual arms simultaneously that performed center-out reaching movements towards two separate targets for each arm. Approximately five hundred neurons were recorded in multiple cortical areas, and an unscented Kalman filter was used for decoding.

Neural Prostheses for Restoration of Locomotion

Invasive NPs for the control of lower limbs have remained underdeveloped for some time. That was because most neurophysiological studies in nonhuman primates have traditionally focused on the upper limb tasks, whereas the control of the lower limbs remained virtually neglected. Only several years ago, NPs have started to develop for restoration of legged locomotion (Cheng, Fitzsimmons et al. 2007, Bouyarmane, Vaillant et al. 2014), and currently we are witnessing a rapid rise in such NPs.
Nathan Fitzsimmons and his colleagues recorded from sensorimotor cortical ensembles in monkey trained to walk bipedally on a treadmill (Fitzsimmons, Lebedev et al. 2009). While the monkeys performed the walking, movements of their lower limbs were monitored using a video tracking system (Peikon, Fitzsimmons et al. 2009). Using these recordings as a training data, multiple Wiener filters were set to reproduce the lower limbs kinematics from the cortical recordings. The Wiener filters also reproduced EMGs of the lower limb muscles. Both forward walking and backward walking were decoded. Next, the researchers sent the decoded kinematic parameters of monkey walking to Kyoto, Japan, where a humanoid robot reproduced the monkey gait at the laboratory of Mitsuo Kawato (Cheng, Fitzsimmons et al. 2007, Kawato 2008). 
These findings highlight the fact that invasive cortical recordings can provide highly efficient signals for the control of devices that restore walking, for example for exoskeletons, such as ExoAtlet (Figure 3). Exoskeletons controlled by invasive NPs, such as cortical microelectrode recordings and ECoG, most certainly will emerge in the near future because both the recording methods (Leuthardt, Miller et al. 2006, Collinger, Wodlinger et al. 2013, Schwarz, Lebedev et al. 2014) and exoskeleton technologies (Farris, Quintero et al. 2012, Frolov, Biriukova et al. 2013, Lisi, Noda et al. 2014, Wall, Borg et al. 2015, Onose, Cârdei et al. 2016) already exist.
One interesting strategy in NPs for locomotion is using cortical signals as a control signal to an electrical stimulator to the spinal cord that evokes walking automatism. The feasibility of such a system was recently demonstrated in a study conducted in rhesus monkeys with partial spinal cord injuries (Capogrosso, Milekovic et al. 2016). In that study, monkeys with spinal cord lesions attempted to walk quadrupedally but experienced deficits in the leg ipsilateral to the lesion site. The researchers alleviated this deficit by decoding the step cycle from motor cortical activity and triggering the spinal cord stimulation at the appropriate phases of the cycle. The stimulation induced near-normal stepping movements in the impaired leg.
Currently, the wheelchair still remains the main means of locomotion for paralyzed patients. Here, invasive NP technology could come handy, particularly for severely paralyzed patients who cannot use their upper limbs to control the wheelchair. Although noninvasive NPs for wheelchair control already exist (Moore 2003, Craig and Nguyen 2007, Galán, Nuttin et al. 2008), invasive NPs could offer much better information transfer rate, reaction time and versatility. A pioneering study of an invasive NP for wheelchair control was conducted by Rajangam and her colleagues who demonstrated that rhesus monkeys could navigate while seating on top of a motorized wheelchair and steering it with their motor cortical activity (Rajangam, Tseng et al. 2016). For this purpose, two Wiener filters were trained; one controlled the linear velocity of the wheelchair (i.e., back and forth movements), and the other controlled rotational velocity (i.e., wheelchair turns). 


 Figure 3. The ExoAtlet. This is an electrically actuated exoskeleton that assists patients suffering from leg paralysis. The ExoAtlet allows to set the stepping parameters and enacts several bipedal states, such as standing, walking on different surfaces and stepping over obstacles. Reproduced with permission from Ekaterina Bereziy, exoatlet.ru.

Figure 3. The ExoAtlet. This is an electrically actuated exoskeleton that assists patients suffering from leg paralysis. The ExoAtlet allows to set the stepping parameters and enacts several bipedal states, such as standing, walking on different surfaces and stepping over obstacles. Reproduced with permission from Ekaterina Bereziy, exoatlet.ru.



Controlling Patient’s Own Muscles

An alternative to using robotic devices is the possibility to reanimate patient’s paralyzed body using functional electrical stimulation (FES) of the muscles. Several FES-based NPs have been already developed.
Efficient NPs of this type should be able to decode muscle-like patterns from the brain activity. The feasibility of such decoding was demonstrated using simultaneous recordings of cortical activity and arm EMGs in monkeys (Morrow and Miller 2003, Santucci, Kralik et al. 2005, Pohlmeyer, Solla et al. 2007, Fitzsimmons, Lebedev et al. 2009). In these experiments, linear decoders successfully reconstructed EMG patterns from cortical activity. Furthermore, experiments in humans showed that multi-channel FES of hand muscles evoked  a variety of movements that approximated normal hand movement(Seifert and Fuglevand 2002, Johnson and Fuglevand 2011).
The first demonstration of a FES-based NP involved EEG recordings (Pfurtscheller, Müller et al. 2003, Pfurtscheller, Rupp et al. 2005). Aided by this NP, a patient learned to control an FES device that animated the paralyzed hand. The patient was able to grip and translate objects. Invasive FES-based NPs were demonstrated in monkeys (Moritz, Perlmutter et al. 2008, Pohlmeyer, Oby et al. 2009, Ethier, Oby et al. 2012). In these experiments, monkey arms were temporarily paralyzed by local anesthetics applied to the nerves. Neuronal activity was recorded in the motor cortex and converted into FES patterns. Monkeys were able to perform motor tasks by putting their arms into action with the FES. And finally, an invasive, FES-based NP restored mobility to the hand of a human patient with a complete spinal cord injury (Bouton, Shaikhouni et al. 2016).

Artificial Somatosensory Sensations

Sensory NPs transmit information from the outside world to the brain (Dobelle 1994, Lebedev and Nicolelis 2006, Nicolelis and Lebedev 2009, Rothschild 2010, Lebedev, Tate et al. 2011, Bensmaia and Miller 2014). Such NPs are intended for people with sensory disabilities. Given the large number of sensory modalities, one can imagine a variety of NPs that restore sight, hearing, tactile sensations, etc. by sending the appropriate information to the corresponding sensory areas of the brain. Sensory NPs could be interfaced to different levels of sensory hierarchy: to the peripheral nerves, spinal cord, thalamus and cortex. Ideally, sensory NPs should account not only for the bottom-up flow of information from the peripheral receptors to the brain, but also for the top-down, anticipatory communications that are known to play an essential role in sensory processing (Lebedev, Denton et al. 1994, Siegel, Körding et al. 2000, Ghazanfar, Krupa et al. 2001, Krupa, Wiest et al. 2004, Gilbert and Sigman 2007, Pais-Vieira, Lebedev et al. 2013, Pezzulo, D'Ausilio et al. 2016). 
Several NPs have been proposed for restoring somatosensory sensations. This work is rooted in the pioneering discoveries made in the beginning of the 20th century on the effects of electrical stimulation of the brain. In 1909, Harvey Cushing discovered that electrical stimulation of the human cortex can evoke somatosensory percepts without inducing limb movements (Cushing 1909). The sensory effects of electrical stimulation were subsequently studied in great detail by Wilder Penfield (Penfield and Boldrey 1937). Penfield’s patients reported sensations of numbness, tingling, and rarely pain after their somatosensory cortex was electrically stimulated with surface electrodes.
Modern stimulation methods are based on microstimulation, that is injection of small currents into the brain tissues using thin electrodes (Bartlett and Doty 1980, Fitzsimmons, Drake et al. 2007, Kim, Callier et al. 2015). Romo and his colleagues employed microstimulation of monkey primary somatosensory cortex to induce sensations comparable by those evoked by vibrotactile stimulation of the hands (Romo, Hernández et al. 1998). Fitzsimmons and his colleagues pioneered the usage of chronic cortical implants for the same purpose (Fitzsimmons, Drake et al. 2007). In those experiments, owl monkey learned progressively more complex discrimination tasks, starting from detecting the mere presence of microstimulation, then discriminating different temporal patterns of microstimulation, and finally discriminating spatiotemporal patterns delivered to the somatosensory cortex through several pairs of electrodes. 
Bidirectional NPs (Figure 1), opened a new chapter in the development of sensory NPs. These systems simultaneously extract motor commands from the brain motor areas and deliver artificial sensory feedback to the sensory areas. O’Doherty and his colleagues implemented bidirectional NPs in monkeys (O'Doherty, Lebedev et al. 2009, O'Doherty, Lebedev et al. 2011). In these experiments, monkeys controlled a virtual arm shown on a computer screen with their motor cortical activity. The monkeys’ task was to search through an array of screen targets with the virtual hand. The targets were visually identical, but they were associated with different artificial tactile sensations produced by microstimulation of the primary somatosensory cortex. The microstimulation started when the monkey placed the virtual hand over a target. Using such a bidirectional NP, monkeys were able to quickly search through up to three targets displayed on the screen.


Brain-nets represent a futuristic development in NPs. These are NPs that incorporate several brains that work like a super-brain, and potentially could even work as a global brain (Kyriazis 2015). The brains included in a brain-net can perform cooperative tasks and exchange information with each other. A pioneering brain-net experiment was conducted in rats (Pais-Vieira, Lebedev et al. 2013). One rat performed a motor task and acted as a transmitter because its brain activity, after moderate processing with a sigmoidal transfer function, was passed to another rat, called receiver. Microstimulation was applied to the receiver’s sensorimotor cortex to deliver the information.
Brain-nets can connect different species, for example they can connect the human brain to animal brain. In one such experiment neural information was transmitted from a human operating an EEG-based NP to an anesthetized rat (Yoo, Kim et al. 2013). The transmitted command triggered an ultrasound stimulator that activated the rat motor cortex and evoked tail movements. In another experiment, information was transferred from the human brain to cockroach brain (Li and Zhang 2016).
Information exchange was also carried out between two human brains. Grau and his colleagues conducted a study, where one subject operated an EEG-based NP, while the second received messages in the form of transcranial magnetic stimulation (TMS) of the visual cortex that evoked phosphenes (Grau, Ginhoux et al. 2014). In a similar experiment, Rao and his colleagues had one subjects operate an EEG-based NP. Whereas TMS was applied to the second subject’s motor cortex, and evoked hand movements (Rao, Stocco et al. 2014). In an even more advanced demonstration, the same group enabled humans to read the mind of the other humans using an interactive question and answer game (Stocco, Prat et al. 2015).
Yet another type of a brain-net, called brain plus the brain interface, was implemented in rhesus monkeys (Ramakrishnan, Ifft et al. 2015). That interface assisted collaboration between the subjects. Several monkeys (two or three) contributed their cortical signals, which resulted in a better control of a single virtual arm.


Overall, we have seen a significant progress in the field of invasive NPs. Improvements in neural recordings methods allow sampling signals of better quality from a larger number of channels. The high channel count translates into improved neural decoding and more accurate control of external devices. Sensory and bidirectional NPs have been developed with the goal of assisting patients with sensory disabilities. Moreover, brain-nets connect the nervous systems of several participants into a higher-order circuit. These trends in invasive NPs will be translated in the future into multiple benefits for the humanity

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Agorelius, J., F. Tsanakalis, A. Friberg, P. T. Thorbergsson, L. Pettersson and J. Schouenborg (2015). "An array of highly flexible electrodes with a tailored configuration locked by gelatin during implantation—initial evaluation in cortex cerebri of awake rats." Frontiers in Neuroscience 9(331).
Agorelius, J., F. Tsanakalis, A. Friberg, P. T. Thorbergsson, L. M. E. Pettersson and J. Schouenborg (2015). "An array of highly flexible electrodes with a tailored configuration locked by gelatin during implantation – initial evaluation in cortex cerebri of awake rats." Frontiers in Neuroscience 9.
Allison, B. Z., E. W. Wolpaw and J. R. Wolpaw (2007). "Brain-computer interface systems: progress and prospects." Expert Rev Med Devices 4(4): 463-474.
Anastassiou, C. A., R. Perin, H. Markram and C. Koch (2011). "Ephaptic coupling of cortical neurons." Nature neuroscience 14(2): 217-223.
Andersen, R. A., J. W. Burdick, S. Musallam, B. Pesaran and J. G. Cham (2004). "Cognitive neural prosthetics." Trends Cogn Sci 8(11): 486-493.
Andersen, R. A., E. J. Hwang and G. H. Mulliken (2010). "Cognitive neural prosthetics." Annu Rev Psychol 61: 169-190, C161-163.
Anderson, D. J., K. Najafi, S. J. Tanghe, D. A. Evans, K. L. Levy, J. F. Hetke, X. Xue, J. J. Zappia and K. D. Wise (1989). "Batch fabricated thin-film electrodes for stimulation of the central auditory system." Biomedical Engineering, IEEE Transactions on 36(7): 693-704.
Astrand, E., C. Wardak and S. Ben Hamed (2014). "Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications." Front Syst Neurosci 8: 144.
Attiah, M. A. and M. J. Farah (2014). "Minds, motherboards, and money: futurism and realism in the neuroethics of BCI technologies." Frontiers in Systems Neuroscience 8(86).
Bartlett, J. R. and R. W. Doty (1980). "An exploration of the ability of macaques to detect microstimulation of striate cortex." Acta Neurobiol Exp (Wars) 40(4): 713-727.
Batista, A. P., B. M. Yu, G. Santhanam, S. I. Ryu, A. Afshar and K. V. Shenoy (2008). "Cortical neural prosthesis performance improves when eye position is monitored." IEEE Trans Neural Syst Rehabil Eng 16(1): 24-31.
Bensmaia, S. J. and L. E. Miller (2014). "Restoring sensorimotor function through intracortical interfaces: progress and looming challenges." Nature reviews Neuroscience 15(5): 313-325.
Berger, T. W., R. E. Hampson, D. Song, A. Goonawardena, V. Z. Marmarelis and S. A. Deadwyler (2011). "A cortical neural prosthesis for restoring and enhancing memory." Journal of Neural Engineering 8(4): 046017.
Birbaumer, N., N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kubler, J. Perelmouter, E. Taub and H. Flor (1999). "A spelling device for the paralysed." Nature 398(6725): 297-298.
Blazquez, P. M., N. Fujii, J. Kojima and A. M. Graybiel (2002). "A network representation of response probability in the striatum." Neuron 33(6): 973-982.
Bleichner, M., Z. Freudenburg, J. Jansma, E. Aarnoutse, M. Vansteensel and N. Ramsey (2016). "Give me a sign: decoding four complex hand gestures based on high-density ECoG." Brain Structure and Function 221(1): 203-216.
Boniface, S. and N. Antoun (1997). "Endovascular electroencephalography: the technique and its application during carotid amytal assessment." Journal of Neurology, Neurosurgery & Psychiatry 62(2): 193-195.
Borghi, T., A. Bonfanti, G. Zambra, R. Gusmeroli, A. Spinelli and G. Baranauskas (2007). A compact multichannel system for acquisition and processing of neural signals. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.
Bouton, C. E., A. Shaikhouni, N. V. Annetta, M. A. Bockbrader, D. A. Friedenberg, D. M. Nielson, G. Sharma, P. B. Sederberg, B. C. Glenn, W. J. Mysiw, A. G. Morgan, M. Deogaonkar and A. R. Rezai (2016). "Restoring cortical control of functional movement in a human with quadriplegia." Nature.
Bouyarmane, K., J. Vaillant, N. Sugimoto, F. Keith, J.-i. Furukawa and J. Morimoto (2014). "Brain-machine interfacing control of whole-body humanoid motion." Frontiers in Systems Neuroscience 8(138).
Bozinovski, S., M. Sestakov and L. Bozinovska (1988). Using EEG alpha rhythm to control a mobile robot. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Brindley, G. S. (1970). "Sensations produced by electrical stimulation of the occipital poles of the cerebral hemispheres, and their use in constructing visual prostheses." Ann R Coll Surg Engl 47(2): 106-108.
Brindley, G. S. and W. S. Lewin (1968). "The sensations produced by electrical stimulation of the visual cortex." J Physiol 196(2): 479-493.
Brindley, G. S. and W. S. Lewin (1968). "The visual sensations produced by electrical stimulation of the medial occipital cortex." J Physiol 194(2): 54-55P.
Brumberg, J. S., A. Nieto-Castanon, P. R. Kennedy and F. H. Guenther (2010). "Brain-Computer Interfaces for Speech Communication." Speech Commun 52(4): 367-379.
Brunner, P., A. L. Ritaccio, J. F. Emrich, H. Bischof and G. Schalk (2011). "Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG)." Front Neurosci 5: 5.
Buzsaki, G., R. G. Bickford, L. J. Ryan, S. Young, O. Prohaska, R. J. Mandel and F. H. Gage (1989). "Multisite recording of brain field potentials and unit activity in freely moving rats." J Neurosci Methods 28(3): 209-217.
Campbell, P. K., K. E. Jones, R. J. Huber, K. W. Horch and R. A. Normann (1991). "A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array." Biomedical Engineering, IEEE Transactions on 38(8): 758-768.
Capogrosso, M., T. Milekovic, D. Borton, F. Wagner, E. M. Moraud, J.-B. Mignardot, N. Buse, J. Gandar, Q. Barraud and D. Xing (2016). "A brain–spine interface alleviating gait deficits after spinal cord injury in primates." Nature 539(7628): 284-288.
Carmena, J. M., M. A. Lebedev, R. E. Crist, J. E. O'Doherty, D. M. Santucci, D. F. Dimitrov, P. G. Patil, C. S. Henriquez and M. A. Nicolelis (2003). "Learning to control a brain-machine interface for reaching and grasping by primates." PLoS Biol 1(2): E42.
Chapin, J. K., K. A. Moxon, R. S. Markowitz and M. A. Nicolelis (1999). "Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex." Nat Neurosci 2(7): 664-670.
Cheng, G., N. Fitzsimmons, J. Morimoto, M. Lebedev, M. Kawato and M. Nicolelis (2007). Bipedal locomotion with a humanoid robot controlled by cortical ensemble activity. Abstr. Soc. Neurosci.
Chestek, C. A., V. Gilja, P. Nuyujukian, R. J. Kier, F. Solzbacher, S. I. Ryu, R. R. Harrison and K. V. Shenoy (2009). "HermesC: low-power wireless neural recording system for freely moving primates." IEEE Trans Neural Syst Rehabil Eng 17(4): 330-338.
Christie, B. P., K. R. Ashmont, P. A. House and B. Greger (2016). "Approaches to a cortical vision prosthesis: implications of electrode size and placement." Journal of neural engineering 13(2): 025003.
Collinger, J. L., B. Wodlinger, J. E. Downey, W. Wang, E. C. Tyler-Kabara, D. J. Weber, A. J. McMorland, M. Velliste, M. L. Boninger and A. B. Schwartz (2013). "High-performance neuroprosthetic control by an individual with tetraplegia." Lancet 381(9866): 557-564.
Collins, W. R., Jr., F. E. Nulsen and C. T. Randt (1960). "Relation of peripheral nerve fiber size and sensation in man." Arch Neurol 3: 381-385.
Connors, B. W. and M. A. Long (2004). "Electrical synapses in the mammalian brain." Annu Rev Neurosci 27: 393-418.
Craggs, M. D. (1975). "Cortical control of motor prostheses: using the cord-transected baboon as the primate model for human paraplegia." Adv Neurol 10: 91-101.
Craig, D. A. and H. T. Nguyen (2007). "Adaptive EEG thought pattern classifier for advanced wheelchair control." Conf Proc IEEE Eng Med Biol Soc 2007: 2544-2547.
Crone, N. E., A. Sinai and A. Korzeniewska (2006). "High-frequency gamma oscillations and human brain mapping with electrocorticography." Progress in brain research 159: 275-295.
Cushing, H. (1909). "A note upon the faradic stimulation of the postcentral gyrus in conscious patients. 1." Brain 32(1): 44-53.
Dahl, W. D. (1962). "An alpha rhythm feedback control unit." Rep U S Nav Med Res Lab 5848: 20.
Dangi, S., S. Gowda, H. G. Moorman, A. L. Orsborn, K. So, M. Shanechi and J. M. Carmena (2014). "Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces." Neural Comput 26(9): 1811-1839.
Deadwyler, S., R. Hampson, A. Sweat, D. Song, R. Chan, I. Opris, G. Gerhardt, V. Marmarelis and T. Berger (2013). "Donor/recipient enhancement of memory in rat hippocampus." Frontiers in Systems Neuroscience 7(120).
Dennett, D. C. (1991). Consciousness explained. Boston, Little, Brown and Co.
DiGiovanna, J., B. Mahmoudi, J. Fortes, J. C. Principe and J. C. Sanchez (2009). "Coadaptive brain–machine interface via reinforcement learning." IEEE transactions on biomedical engineering 56(1): 54-64.
Djourno, A. and C. Eyriès (1957). "Prothese auditive par excitation electrique a distance du nerf sensoriel a laide dun bobinage inclus a demeure." Presse médicale 65(63): 1417-1417.
Dobelle, W. H. (1994). "Artificial vision for the blind. The summit may be closer than you think." ASAIO J 40(4): 919-922.
Dobelle, W. H. and M. G. Mladejovsky (1974). "Phosphenes produced by electrical stimulation of human occipital cortex, and their application to the development of a prosthesis for the blind." J Physiol 243(2): 553-576.
Dobelle, W. H., M. G. Mladejovsky, J. R. Evans, T. S. Roberts and J. P. Girvin (1976). ""Braille" reading by a blind volunteer by visual cortex stimulation." Nature 259(5539): 111-112.
Dobelle, W. H., M. G. Mladejovsky and J. P. Girvin (1974). "Artifical vision for the blind: electrical stimulation of visual cortex offers hope for a functional prosthesis." Science 183(4123): 440-444.
Dobelle, W. H., D. O. Quest, J. L. Antunes, T. S. Roberts and J. P. Girvin (1979). "Artificial vision for the blind by electrical stimulation of the visual cortex." Neurosurgery 5(4): 521-527.
Donchin, E., K. M. Spencer and R. Wijesinghe (2000). "The mental prosthesis: assessing the speed of a P300-based brain-computer interface." IEEE Trans Rehabil Eng 8(2): 174-179.
Ethier, C., E. R. Oby, M. Bauman and L. E. Miller (2012). "Restoration of grasp following paralysis through brain-controlled stimulation of muscles." Nature 485(7398): 368-371.
Evans, J. R. and A. Abarbanel (1999). Introduction to quantitative EEG and neurofeedback, Elsevier.
Evarts, E. V. (1964). "Temporal Patterns of Discharge of Pyramidal Tract Neurons during Sleep and Waking in the Monkey." J Neurophysiol 27: 152-171.
Evarts, E. V. (1966). "Pyramidal tract activity associated with a conditioned hand movement in the monkey." J Neurophysiol 29(6): 1011-1027.
Evarts, E. V., E. Bental, B. Bihari and P. R. Huttenlocher (1962). "Spontaneous discharge of single neurons during sleep and waking." Science 135(3505): 726-728.
Farah, M. J. and P. R. Wolpe (2004). "Monitoring and manipulating brain function: New neuroscience technologies and their ethical implications." Hastings Center Report 34(3): 35-45.
Farris, R. J., H. A. Quintero and M. Goldfarb (2012). "Performance evaluation of a lower limb exoskeleton for stair ascent and descent with paraplegia." Conf Proc IEEE Eng Med Biol Soc 2012: 1908-1911.
Fetz, E. E. (1969). "Operant conditioning of cortical unit activity." Science 163(3870): 955-958.
Fetz, E. E. (1992). "Are movement parameters recognizably coded in the activity of single neurons?" Behavioral and brain sciences 15(04): 679-690.
Finger, S. (1994). Origins of neuroscience : a history of explorations into brain function. New York, Oxford University Press.
Finke, A., A. Lenhardt and H. Ritter (2009). "The MindGame: a P300-based brain-computer interface game." Neural Netw 22(9): 1329-1333.
Fitzsimmons, N. A., W. Drake, T. L. Hanson, M. A. Lebedev and M. A. Nicolelis (2007). "Primate reaching cued by multichannel spatiotemporal cortical microstimulation." J Neurosci 27(21): 5593-5602.
Fitzsimmons, N. A., M. A. Lebedev, I. D. Peikon and M. A. Nicolelis (2009). "Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity." Front Integr Neurosci 3: 3.
Frank, K. (1968). "Some approaches to the technical problem of chronic excitation of peripheral nerve." Ann. Otol. Rhinol. Laryngol. 77: 761–771.
Friston, K. (2009). "The free-energy principle: a rough guide to the brain?" Trends Cogn Sci 13(7): 293-301.
Friston, K. (2010). "The free-energy principle: a unified brain theory?" Nat Rev Neurosci 11(2): 127-138.
Friston, K., J. Kilner and L. Harrison (2006). "A free energy principle for the brain." J Physiol Paris 100(1-3): 70-87.
Frolov, A. A., E. V. Biriukova, P. D. Bobrov, O. A. Mokienko, A. K. Platonov, V. E. Prianichnikov and L. A. Chernikov (2013). "[Principles of neurorehabilitation based on brain-computer interface and biologically plausible control of the exoskeleton]." Fiziol Cheloveka 39(2): 99-113.
Galán, F., M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips and J. d. R. Millán (2008). "A brain-actuated wheelchair: asynchronous and non-invasive brain–computer interfaces for continuous control of robots." Clinical Neurophysiology 119(9): 2159-2169.
Ganguly, K. and J. M. Carmena (2009). "Emergence of a stable cortical map for neuroprosthetic control." PLoS Biol 7(7): e1000153.
Garces Correa, A., L. Orosco and E. Laciar (2014). "Automatic detection of drowsiness in EEG records based on multimodal analysis." Med Eng Phys 36(2): 244-249.
Georgopoulos, A. P. (1994). "Population activity in the control of movement." Int Rev Neurobiol 37: 103-119; discussion 121-103.
Georgopoulos, A. P., R. E. Kettner and A. B. Schwartz (1988). "Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population." J Neurosci 8(8): 2928-2937.
Georgopoulos, A. P., J. T. Lurito, M. Petrides, A. B. Schwartz and J. T. Massey (1989). "Mental rotation of the neuronal population vector." Science 243(4888): 234-236.
Georgopoulos, A. P., A. B. Schwartz and R. E. Kettner (1986). "Neuronal population coding of movement direction." Science 233(4771): 1416-1419.
Ghazanfar, A. A., D. J. Krupa and M. A. Nicolelis (2001). "Role of cortical feedback in the receptive field structure and nonlinear response properties of somatosensory thalamic neurons." Exp Brain Res 141(1): 88-100.
Gilbert, C. D. and M. Sigman (2007). "Brain states: top-down influences in sensory processing." Neuron 54(5): 677-696.
Glannon, W. (2014). "Ethical issues with brain-computer interfaces." Frontiers in Systems Neuroscience 8(136).
Golgi, C. (1995). Sulla fina anatomia degli organi centrali del sistema nervoso. Firenze, Giunti.
Golub, J. S., J. O. Phillips and J. T. Rubinstein (2011). Vestibular Implants. Auditory Prostheses, Springer: 109-133.
Grau, C., R. Ginhoux, A. Riera, T. L. Nguyen, H. Chauvat, M. Berg, J. L. Amengual, A. Pascual-Leone and G. Ruffini (2014). "Conscious brain-to-brain communication in humans using non-invasive technologies." PLoS One 9(8): e105225.
Grewe, B. F., D. Langer, H. Kasper, B. M. Kampa and F. Helmchen (2010). "High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision." Nature methods 7(5): 399-405.
Grinvald, A., R. Frostig, E. Lieke and R. Hildesheim (1988). "Optical imaging of neuronal activity." Physiological reviews 68(4): 1285-1366.
Grinvald, A. and R. Hildesheim (2004). "VSDI: a new era in functional imaging of cortical dynamics." Nature Reviews Neuroscience 5(11): 874-885.
Gross, C. G. (2002). "Genealogy of the “grandmother cell”." The Neuroscientist 8(5): 512-518.
Gualtierotti, T. and P. Bailey (1968). "A neutral buoyancy micro-electrode for prolonged recording from single nerve units." Electroencephalography and clinical neurophysiology 25(1): 77-81.
Guenther, F. H., J. S. Brumberg, E. J. Wright, A. Nieto-Castanon, J. A. Tourville, M. Panko, R. Law, S. A. Siebert, J. L. Bartels, D. S. Andreasen, P. Ehirim, H. Mao and P. R. Kennedy (2009). "A wireless brain-machine interface for real-time speech synthesis." PLoS One 4(12): e8218.
Harrison, R. R., R. J. Kier, C. A. Chestek, V. Gilja, P. Nuyujukian, S. Ryu, B. Greger, F. Solzbacher and K. V. Shenoy (2009). "Wireless neural recording with single low-power integrated circuit." IEEE Trans Neural Syst Rehabil Eng 17(4): 322-329.
Hassler, C., J. Guy, M. Nietzschmann, J. F. Staiger and T. Stieglitz (2011). Chronic intracortical implantation of saccharose-coated flexible shaft electrodes into the cortex of rats. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, IEEE.
Haykin, S. S. (2014). Adaptive filter theory. Upper Saddle River, New Jersey, Pearson.
Hensel, H. and K. K. Boman (1960). "Afferent impulses in cutaneous sensory nerves in human subjects." J Neurophysiol 23: 564-578.
Hildt, E. (2015). "What will this do to me and my brain? Ethical issues in brain-to-brain interfacing." Frontiers in Systems Neuroscience 9(17).
Hill, N. J., D. Gupta, P. Brunner, A. Gunduz, M. A. Adamo, A. Ritaccio and G. Schalk (2012). "Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping." J Vis Exp(64).
Hochberg, L. R., D. Bacher, B. Jarosiewicz, N. Y. Masse, J. D. Simeral, J. Vogel, S. Haddadin, J. Liu, S. S. Cash, P. van der Smagt and J. P. Donoghue (2012). "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm." Nature 485(7398): 372-375.
Hochberg, L. R., M. D. Serruya, G. M. Friehs, J. A. Mukand, M. Saleh, A. H. Caplan, A. Branner, D. Chen, R. D. Penn and J. P. Donoghue (2006). "Neuronal ensemble control of prosthetic devices by a human with tetraplegia." Nature 442(7099): 164-171.
Houk, J. C. and S. P. Wise (1995). "Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: their role in planning and controlling action." Cereb Cortex 5(2): 95-110.
House, W. H. (1976). "Cochlear implants." Ann Otol Rhinol Laryngol 85(suppl 27): 3-91.
Humphrey, D. R., E. M. Schmidt and W. D. Thompson (1970). "Predicting measures of motor performance from multiple cortical spike trains." Science 170(3959): 758-762.
Ifft, P. J., S. Shokur, Z. Li, M. A. Lebedev and M. A. Nicolelis (2013). "A brain-machine interface enables bimanual arm movements in monkeys." Sci Transl Med 5(210): 210ra154.
Jarvis, S. and S. R. Schultz (2015). "Prospects for Optogenetic Augmentation of Brain Function." Frontiers in Systems Neuroscience 9(157).
Jog, M., C. Connolly, Y. Kubota, D. Iyengar, L. Garrido, R. Harlan and A. Graybiel (2002). "Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques." Journal of neuroscience methods 117(2): 141-152.
Johnson, L. A. and A. J. Fuglevand (2011). "Mimicking muscle activity with electrical stimulation." Journal of neural engineering 8(1): 016009.
Kalman, R. E. (1960). "A new approach to linear filtering and prediction problems." Journal of basic Engineering 82(1): 35-45.
Kalman, R. E. and R. S. Bucy (1961). "New results in linear filtering and prediction theory." Journal of basic engineering 83(1): 95-108.
Kamiya, J. (1971). "Biofeedback training in voluntary control of EEG alpha rhythms." Calif Med 115(3): 44.
Kaplan, B. J. (1975). "Biofeedback in epileptics: equivocal relationship of reinforced EEG frequency to seizure reduction." Epilepsia 16(3): 477-485.
Kawato, M. (2008). "Brain controlled robots."
Kennedy, P. R. (1989). "The cone electrode: a long-term electrode that records from neurites grown onto its recording surface." J Neurosci Methods 29(3): 181-193.
Kennedy, P. R., R. A. Bakay and S. M. Sharpe (1992). "Behavioral correlates of action potentials recorded chronically inside the Cone Electrode." Neuroreport 3(7): 605-608.
Kennedy, P. R., S. S. Mirra and R. A. Bakay (1992). "The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex." Neurosci Lett 142(1): 89-94.
Kettner, R. E., A. B. Schwartz and A. P. Georgopoulos (1988). "Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins." J Neurosci 8(8): 2938-2947.
Kim, S., R. Bhandari, M. Klein, S. Negi, L. Rieth, P. Tathireddy, M. Toepper, H. Oppermann and F. Solzbacher (2009). "Integrated wireless neural interface based on the Utah electrode array." Biomedical microdevices 11(2): 453-466.
Kim, S., T. Callier, G. A. Tabot, F. V. Tenore and S. J. Bensmaia (2015). "Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes." Frontiers in Systems Neuroscience 9(47).
Kim, S. P., J. C. Sanchez, Y. N. Rao, D. Erdogmus, J. M. Carmena, M. A. Lebedev, M. A. Nicolelis and J. C. Principe (2006). "A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces." J Neural Eng 3(2): 145-161.
Kowalski, K. C., B. D. He and L. Srinivasan (2013). "Dynamic analysis of naive adaptive brain-machine interfaces." Neural Comput 25(9): 2373-2420.
Kozai, T. D. Y. and D. R. Kipke (2009). "Insertion shuttle with carboxyl terminated self-assembled monolayer coatings for implanting flexible polymer neural probes in the brain." Journal of neuroscience methods 184(2): 199-205.
Kralik, J. D., D. F. Dimitrov, D. J. Krupa, D. B. Katz, D. Cohen and M. A. Nicolelis (2001). "Techniques for long-term multisite neuronal ensemble recordings in behaving animals." Methods 25(2): 121-150.
Krupa, D. J., M. C. Wiest, M. G. Shuler, M. Laubach and M. A. Nicolelis (2004). "Layer-specific somatosensory cortical activation during active tactile discrimination." Science 304(5679): 1989-1992.
Kwon, K. Y., H.-M. Lee, M. Ghovanloo, A. Weber and W. Li (2015). "Design, fabrication, and packaging of an integrated, wirelessly-powered optrode array for optogenetics application." Frontiers in Systems Neuroscience 9(69).
Kyriazis, M. (2015). "Systems neuroscience in focus: from the human brain to the global brain?" Frontiers in Systems Neuroscience 9(7).
Lebedev, M. A. (2014). "How to read neuron-dropping curves?" Front Syst Neurosci 8: 102.
Lebedev, M. A., R. E. Crist and M. A. L. Nicolelis (2008). Building Brain-Machine Interfaces to Restore Neurological Functions. Methods for Neural Ensemble Recordings. M. A. L. Nicolelis. Boca Raton (FL).
Lebedev, M. A., J. M. Denton and R. J. Nelson (1994). "Vibration-entrained and premovement activity in monkey primary somatosensory cortex." J Neurophysiol 72(4): 1654-1673.
Lebedev, M. A. and M. A. Nicolelis (2006). "Brain-machine interfaces: past, present and future." Trends Neurosci 29(9): 536-546.
Lebedev, M. A., A. J. Tate, T. L. Hanson, Z. Li, J. E. O'Doherty, J. A. Winans, P. J. Ifft, K. Z. Zhuang, N. A. Fitzsimmons, D. A. Schwarz, A. M. Fuller, J. H. An and M. A. Nicolelis (2011). "Future developments in brain-machine interface research." Clinics (Sao Paulo) 66 Suppl 1: 25-32.
Lee, P. L., J. J. Sie, Y. J. Liu, C. H. Wu, M. H. Lee, C. H. Shu, P. H. Li, C. W. Sun and K. K. Shyu (2010). "An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system." Ann Biomed Eng 38(7): 2383-2397.
Leuthardt, E. C., K. J. Miller, G. Schalk, R. P. Rao and J. G. Ojemann (2006). "Electrocorticography-based brain computer interface--the Seattle experience." IEEE Trans Neural Syst Rehabil Eng 14(2): 194-198.
Lewis, P. M., L. N. Ayton, R. H. Guymer, A. J. Lowery, P. J. Blamey, P. J. Allen, C. D. Luu and J. V. Rosenfeld (2016). "Advances in implantable bionic devices for blindness: a review." ANZ Journal of Surgery 86(9): 654-659.
Lewis, T. J. and J. Rinzel (2003). "Dynamics of spiking neurons connected by both inhibitory and electrical coupling." J Comput Neurosci 14(3): 283-309.
Li, G. and D. Zhang (2016). "Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain." PloS one 11(3): e0150667.
Li, Z. (2014). "Decoding methods for neural prostheses: where have we reached?" Front Syst Neurosci 8: 129.
Li, Z., J. E. O'Doherty, T. L. Hanson, M. A. Lebedev, C. S. Henriquez and M. A. Nicolelis (2009). "Unscented Kalman filter for brain-machine interfaces." PLoS One 4(7): e6243.
Li, Z., J. E. O'Doherty, M. A. Lebedev and M. A. Nicolelis (2011). "Adaptive decoding for brain-machine interfaces through Bayesian parameter updates." Neural Comput 23(12): 3162-3204.
Libet, B., W. W. Alberts, E. W. Wright, Jr., L. D. Delattre, G. Levin and B. Feinstein (1964). "Production of Threshold Levels of Conscious Sensation by Electrical Stimulation of Human Somatosensory Cortex." J Neurophysiol 27: 546-578.
Lisi, G., T. Noda and J. Morimoto (2014). "Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot." Frontiers in Systems Neuroscience 8(85).
Liu, N. H., C. Y. Chiang and H. M. Hsu (2013). "Improving driver alertness through music selection using a mobile EEG to detect brainwaves." Sensors (Basel) 13(7): 8199-8221.
Llinas, R. R., K. D. Walton, M. Nakao, I. Hunter and P. A. Anquetil (2005). "Neuro-vascular central nervous recording/stimulating system: Using nanotechnology probes." Journal of Nanoparticle Research 7(2-3): 111-127.
Madan, C. R. (2014). "Augmented memory: a survey of the approaches to remembering more." Frontiers in systems neuroscience 8: 30.
Maguire, G. Q. and E. M. McGee (1999). "Implantable brain chips? Time for debate." Hastings Center Report 29(1): 7-13.
Mahmoudi, B., E. A. Pohlmeyer, N. W. Prins, S. Geng and J. C. Sanchez (2013). "Towards autonomous neuroprosthetic control using Hebbian reinforcement learning." Journal of neural engineering 10(6): 066005.
Marchesi, M. and B. Riccò (2013). BRAVO: a brain virtual operator for education exploiting brain-computer interfaces. CHI'13 Extended Abstracts on Human Factors in Computing Systems, ACM.
Martisius, I. and R. Damasevicius (2016). "A Prototype SSVEP Based Real Time BCI Gaming System." Comput Intell Neurosci 2016: 3861425.
Mason, S. G., R. Bohringer, J. F. Borisoff and G. E. Birch (2004). "Real-time control of a video game with a direct brain--computer interface." J Clin Neurophysiol 21(6): 404-408.
Maynard, E. M., C. T. Nordhausen and R. A. Normann (1997). "The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces." Electroencephalogr Clin Neurophysiol 102(3): 228-239.
Miller, K. J., P. Shenoy, J. W. Miller, R. P. Rao and J. G. Ojemann (2007). "Real-time functional brain mapping using electrocorticography." Neuroimage 37(2): 504-507.
Montijn, J. S., M. Vinck and C. M. Pennartz (2014). "Population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation." Front Comput Neurosci 8: 58.
Moore, M. M. (2003). "Real-world applications for brain-computer interface technology." IEEE Trans Neural Syst Rehabil Eng 11(2): 162-165.
Moritz, C. T., S. I. Perlmutter and E. E. Fetz (2008). "Direct control of paralysed muscles by cortical neurons." Nature 456(7222): 639-642.
Morizio, J., P. Irazoqui, V. Go and J. Parmentier (2005). Wireless headstage for neural prosthetics. Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005., IEEE.
Morrow, M. M. and L. E. Miller (2003). "Prediction of muscle activity by populations of sequentially recorded primary motor cortex neurons." Journal of neurophysiology 89(4): 2279-2288.
Musallam, S., M. J. Bak, P. R. Troyk and R. A. Andersen (2007). "A floating metal microelectrode array for chronic implantation." Journal of neuroscience methods 160(1): 122-127.
Musallam, S., B. Corneil, B. Greger, H. Scherberger and R. Andersen (2004). "Cognitive control signals for neural prosthetics." Science 305(5681): 258-262.
Musallam, S., B. D. Corneil, B. Greger, H. Scherberger and R. A. Andersen (2004). "Cognitive control signals for neural prosthetics." Science 305(5681): 258-262.
Najafi, K., K. D. Wise and T. Mochizuki (1985). "A high-yield IC-compatible multichannel recording array." Electron Devices, IEEE Transactions on 32(7): 1206-1211.
Neves, H., G. Orban, M. Koudelka-Hep, T. Stieglitz and P. Ruther (2007). Development of modular multifunctional probe arrays for cerebral applications. Neural Engineering, 2007. CNE'07. 3rd International IEEE/EMBS Conference on, IEEE.
Nicolelis, M. A., L. A. Baccala, R. C. Lin and J. K. Chapin (1995). "Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system." Science 268(5215): 1353-1358.
Nicolelis, M. A., A. A. Ghazanfar, B. M. Faggin, S. Votaw and L. M. Oliveira (1997). "Reconstructing the engram: simultaneous, multisite, many single neuron recordings." Neuron 18(4): 529-537.
Nicolelis, M. A., A. A. Ghazanfar, C. R. Stambaugh, L. M. Oliveira, M. Laubach, J. K. Chapin, R. J. Nelson and J. H. Kaas (1998). "Simultaneous encoding of tactile information by three primate cortical areas." Nat Neurosci 1(7): 621-630.
Nicolelis, M. A. and M. A. Lebedev (2009). "Principles of neural ensemble physiology underlying the operation of brain-machine interfaces." Nat Rev Neurosci 10(7): 530-540.
Nicolelis, M. A., R. C. Lin, D. J. Woodward and J. K. Chapin (1993). "Dynamic and distributed properties of many-neuron ensembles in the ventral posterior medial thalamus of awake rats." Proc Natl Acad Sci U S A 90(6): 2212-2216.
Nicolelis, M. A., R. C. Lin, D. J. Woodward and J. K. Chapin (1993). "Induction of immediate spatiotemporal changes in thalamic networks by peripheral block of ascending cutaneous information." Nature 361(6412): 533-536.
Nordhausen, C. T., E. M. Maynard and R. A. Normann (1996). "Single unit recording capabilities of a 100 microelectrode array." Brain Res 726(1-2): 129-140.
O'Doherty, J. E., M. A. Lebedev, T. L. Hanson, N. A. Fitzsimmons and M. A. Nicolelis (2009). "A brain-machine interface instructed by direct intracortical microstimulation." Front Integr Neurosci 3: 20.
O'Doherty, J. E., M. A. Lebedev, P. J. Ifft, K. Z. Zhuang, S. Shokur, H. Bleuler and M. A. Nicolelis (2011). "Active tactile exploration using a brain-machine-brain interface." Nature 479(7372): 228-231.
Obeid, I., M. A. Nicolelis and P. D. Wolf (2004). "A multichannel telemetry system for single unit neural recordings." J Neurosci Methods 133(1-2): 33-38.
Obermaier, B., C. Neuper, C. Guger and G. Pfurtscheller (2001). "Information transfer rate in a five-classes brain-computer interface." IEEE Trans Neural Syst Rehabil Eng 9(3): 283-288.
Okorokova, E., M. Lebedev, M. Linderman and A. Ossadtchi (2015). "A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings." Frontiers in Neuroscience 9(389).
Onose, G., V. Cârdei, Ş. T. Crăciunoiu, V. Avramescu, I. Opriş, M. A. Lebedev and M. V. Constantinescu (2016). "Mechatronic Wearable Exoskeletons for Bionic Bipedal Standing and Walking: A New Synthetic Approach." Frontiers in Neuroscience 10(343).
Ordikhani-Seyedlar, M., M. A. Lebedev, H. B. D. Sorensen and S. Puthusserypady (2016). "Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges." Frontiers in Neuroscience 10(352).
Orsborn, A. L., S. Dangi, H. G. Moorman and J. M. Carmena (2012). "Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions." IEEE Trans Neural Syst Rehabil Eng 20(4): 468-477.
Oxley, T. J., N. L. Opie, S. E. John, G. S. Rind, S. M. Ronayne, T. L. Wheeler, J. W. Judy, A. J. McDonald, A. Dornom and T. J. Lovell (2016). "Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity." Nature biotechnology.
Pais-Vieira, M., M. Lebedev, C. Kunicki, J. Wang and M. A. Nicolelis (2013). "A brain-to-brain interface for real-time sharing of sensorimotor information." Sci Rep 3: 1319.
Pais-Vieira, M., M. A. Lebedev, M. C. Wiest and M. A. Nicolelis (2013). "Simultaneous top-down modulation of the primary somatosensory cortex and thalamic nuclei during active tactile discrimination." J Neurosci 33(9): 4076-4093.
Patil, P. G., J. M. Carmena, M. A. Nicolelis and D. A. Turner (2004). "Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface." Neurosurgery 55(1): 27-35; discussion 35-28.
Patrick, J., B. Valeur, L. Monnerie and J.-P. Changeux (1971). "Changes in extrinsic fluorescence intensity of the electroplax membrane during electrical excitation." The Journal of membrane biology 5(1): 102-120.
Peikon, I. D., N. A. Fitzsimmons, M. A. Lebedev and M. A. Nicolelis (2009). "Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies." J Neurosci Methods 180(2): 224-233.
Penfield, W. and E. Boldrey (1937). "Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation." Brain: A journal of neurology.
Pezzulo, G., A. D'Ausilio and A. Gaggioli (2016). "Predictive Technologies: Can Smart Tools Augment the Brain's Predictive Abilities?" Frontiers in Neuroscience 10(186).
Pfurtscheller, G., G. R. Müller, J. Pfurtscheller, H. J. Gerner and R. Rupp (2003). "‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia." Neuroscience letters 351(1): 33-36.
Pfurtscheller, G. and C. Neuper (2006). "Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments." Prog Brain Res 159: 433-437.
Pfurtscheller, J., R. Rupp, G. R. Muller, E. Fabsits, G. Korisek, H. J. Gerner and G. Pfurtscheller (2005). "[Functional electrical stimulation instead of surgery? Improvement of grasping function with FES in a patient with C5 tetraplegia]." Unfallchirurg 108(7): 587-590.
Picot, A., S. Charbonnier and A. Caplier (2008). "On-line automatic detection of driver drowsiness using a single electroencephalographic channel." Conf Proc IEEE Eng Med Biol Soc 2008: 3864-3867.
Pohlmeyer, E. A., E. R. Oby, E. J. Perreault, S. A. Solla, K. L. Kilgore, R. F. Kirsch and L. E. Miller (2009). "Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles." PloS one 4(6): e5924.
Pohlmeyer, E. A., S. A. Solla, E. J. Perreault and L. E. Miller (2007). "Prediction of upper limb muscle activity from motor cortical discharge during reaching." Journal of neural engineering 4(4): 369.
Quiroga, R. Q., L. Reddy, G. Kreiman, C. Koch and I. Fried (2005). "Invariant visual representation by single neurons in the human brain." Nature 435(7045): 1102-1107.
Rajangam, S., P. H. Tseng, A. Yin, G. Lehew, D. Schwarz, M. A. Lebedev and M. A. Nicolelis (2016). "Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates." Sci Rep 6: 22170.
Ramakrishnan, A., P. J. Ifft, M. Pais-Vieira, Y. W. Byun, K. Z. Zhuang, M. A. Lebedev and M. A. Nicolelis (2015). "Computing Arm Movements with a Monkey Brainet." Sci Rep 5: 10767.
Ramón y Cajal, S. (1995). Histology of the nervous system of man and vertebrates. New York, Oxford University Press.
Rao, R. P., A. Stocco, M. Bryan, D. Sarma, T. M. Youngquist, J. Wu and C. S. Prat (2014). "A direct brain-to-brain interface in humans." PLoS One 9(11): e111332.
Recce, M. and J. O’keefe (1989). The tetrode: a new technique for multi-unit extracellular recording. Soc Neurosci Abstr.
Romo, R., A. Hernández, A. Zainos and E. Salinas (1998). "Somatosensory discrimination based on cortical microstimulation." Nature 392(6674): 387-390.
Rothschild, R. M. (2010). "Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress." Front Neuroeng 3: 112.
Sakurai, Y. (2014). "Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain." Front Syst Neurosci 8: 104.
Santos, L., I. Opris, J. Fuqua, R. E. Hampson and S. A. Deadwyler (2012). "A novel tetrode microdrive for simultaneous multi-neuron recording from different regions of primate brain." Journal of neuroscience methods 205(2): 368-374.
Santucci, D. M., J. D. Kralik, M. A. Lebedev and M. A. Nicolelis (2005). "Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates." Eur J Neurosci 22(6): 1529-1540.
Schleim, S. (2014). "Whose well-being? Common conceptions and misconceptions in the enhancement debate." Frontiers in Systems Neuroscience 8(148).
Schmidt, E. M. (1980). "Single neuron recording from motor cortex as a possible source of signals for control of external devices." Ann Biomed Eng 8(4-6): 339-349.
Schwartz, A. B., R. E. Kettner and A. P. Georgopoulos (1988). "Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement." J Neurosci 8(8): 2913-2927.
Schwartz, A. B., D. M. Taylor and S. I. Tillery (2001). "Extraction algorithms for cortical control of arm prosthetics." Curr Opin Neurobiol 11(6): 701-707.
Schwarz, D. A., M. A. Lebedev, T. L. Hanson, D. F. Dimitrov, G. Lehew, J. Meloy, S. Rajangam, V. Subramanian, P. J. Ifft, Z. Li, A. Ramakrishnan, A. Tate, K. Z. Zhuang and M. A. Nicolelis (2014). "Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys." Nat Methods 11(6): 670-676.
Seifert, H. M. and A. J. Fuglevand (2002). "Restoration of movement using functional electrical stimulation and Bayes' theorem." The journal of Neuroscience 22(21): 9465-9474.
Sellers, E. W., D. J. Krusienski, D. J. McFarland, T. M. Vaughan and J. R. Wolpaw (2006). "A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance." Biol Psychol 73(3): 242-252.
Seo, D., J. M. Carmena, J. M. Rabaey, M. M. Maharbiz and E. Alon (2015). "Model validation of untethered, ultrasonic neural dust motes for cortical recording." J Neurosci Methods 244: 114-122.
Seo, D., R. M. Neely, K. Shen, U. Singhal, E. Alon, J. M. Rabaey, J. M. Carmena and M. M. Maharbiz (2016). "Wireless recording in the peripheral nervous system with ultrasonic neural dust." Neuron 91(3): 529-539.
Serruya, M., A. Shaikhouni and J. Donoghue (2003). Neural decoding of cursor motion using a Kalman filter. Advances in Neural Information Processing Systems 15: Proceedings of the 2002 Conference, MIT Press.
Serruya, M. D. (2015). "As we may think and be: brain-computer interfaces to expand the substrate of mind." Frontiers in Systems Neuroscience 9(53).
Serruya, M. D., N. G. Hatsopoulos, L. Paninski, M. R. Fellows and J. P. Donoghue (2002). "Instant neural control of a movement signal." Nature 416(6877): 141-142.
Shanechi, M. M., A. Orsborn, H. Moorman, S. Gowda and J. M. Carmena (2014). "High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder." Conf Proc IEEE Eng Med Biol Soc 2014: 6493-6496.
Shkel, A. M. and F.-G. Zeng (2006). "An electronic prosthesis mimicking the dynamic vestibular function." Audiology and Neurotology 11(2): 113-122.
Siegel, M., K. P. Körding and P. König (2000). "Integrating top-down and bottom-up sensory processing by somato-dendritic interactions." Journal of computational neuroscience 8(2): 161-173.
Simmons, F. B., C. J. Mongeon, W. R. Lewis and D. A. Huntington (1964). "Electrical Stimulation of Acoustical Nerve and Inferior Colliculus." Arch Otolaryngol 79: 559-568.
Smetters, D., A. Majewska and R. Yuste (1999). "Detecting action potentials in neuronal populations with calcium imaging." Methods 18(2): 215-221.
Smith, K. U. and S. D. Ansell (1965). "Closed-Loop Digital Computer System for Study of Sensory Feedback Effects of Brain Rhythms." Am J Phys Med 44: 125-137.
Sohal, H. S., A. Jackson, R. Jackson, G. J. Clowry, K. Vaisilevskiy, A. O'Neill and S. Baker (2014). "The Sinusoidal Probe: a new approach to improve electrode longevity." Frontiers in Neuroengineering 7.
Song, D., M. Harway, V. Z. Marmarelis, R. E. Hampson, S. A. Deadwyler and T. W. Berger (2014). "Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling." Frontiers in Systems Neuroscience 8(97).
Spieth, S., O. Brett, K. Seidl, A. Aarts, M. Erismis, S. Herwik, F. Trenkle, S. Tätzner, J. Auber and M. Daub (2011). "A floating 3D silicon microprobe array for neural drug delivery compatible with electrical recording." Journal of Micromechanics and Microengineering 21(12): 125001.
Sterman, M. B. (1973). "Neurophysiologic and clinical studies of sensorimotor EEG biofeedback training: some effects on epilepsy." Semin Psychiatry 5(4): 507-525.
Stocco, A., C. S. Prat, D. M. Losey, J. A. Cronin, J. Wu, J. A. Abernethy and R. P. Rao (2015). "Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface." PloS one 10(9): e0137303.
Stosiek, C., O. Garaschuk, K. Holthoff and A. Konnerth (2003). "In vivo two-photon calcium imaging of neuronal networks." Proceedings of the National Academy of Sciences 100(12): 7319-7324.
Suminski, A. J., A. H. Fagg, F. R. Willett, M. Bodenhamer and N. G. Hatsopoulos (2013). Online adaptive decoding of intended movements with a hybrid kinetic and kinematic brain machine interface. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.
Suter, S. (1977). "Independent biofeedback self-regulation of EEG alpha and skin resistance." Biofeedback Self Regul 2(3): 255-258.
Takeuchi, S., T. Suzuki, K. Mabuchi and H. Fujita (2003). "3D flexible multichannel neural probe array." Journal of micromechanics and microengineering 14(1): 104.
Tasaki, I., A. Watanabe, R. Sandlin and L. Carnay (1968). "Changes in fluorescence, turbidity, and birefringence associated with nerve excitation." Proceedings of the National Academy of Sciences of the United States of America 61(3): 883.
Taylor, D. M., S. I. Tillery and A. B. Schwartz (2002). "Direct cortical control of 3D neuroprosthetic devices." Science 296(5574): 1829-1832.
Tozzi, A., M. Zare and A. A. Benasich (2016). "NEW PERSPECTIVES ON SPONTANEOUS BRAIN ACTIVITY: DYNAMIC NETWORKS AND ENERGY MATTER." Frontiers in Human Neuroscience 10.
Vassanelli, S. and M. Mahmud (2016). "Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“Brain Inspired Systems” Communication." Frontiers in Neuroscience 10(438).
Velliste, M., S. Perel, M. C. Spalding, A. S. Whitford and A. B. Schwartz (2008). "Cortical control of a prosthetic arm for self-feeding." Nature 453(7198): 1098-1101.
Vetter, R. J., J. C. Williams, J. F. Hetke, E. A. Nunamaker and D. R. Kipke (2004). "Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex." Biomedical Engineering, IEEE Transactions on 51(6): 896-904.
Vidal, J.-J. (1973). "Toward direct brain-computer communication." Annual review of Biophysics and Bioengineering 2(1): 157-180.
Viventi, J., D.-H. Kim, L. Vigeland, E. S. Frechette, J. A. Blanco, Y.-S. Kim, A. E. Avrin, V. R. Tiruvadi, S.-W. Hwang and A. C. Vanleer (2011). "Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo." Nature neuroscience 14(12): 1599-1605.
Waldert, S. (2016). "Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?" Frontiers in Neuroscience 10(295).
Wall, A., J. Borg and S. Palmcrantz (2015). "Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review." Frontiers in Systems Neuroscience 9(48).
Wang, W., A. D. Degenhart, J. L. Collinger, R. Vinjamuri, G. P. Sudre, P. D. Adelson, D. L. Holder, E. C. Leuthardt, D. W. Moran and M. L. Boninger (2009). Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.
Weiland, J. D. and D. J. Anderson (2000). "Chronic neural stimulation with thin-film, iridium oxide electrodes." Biomedical Engineering, IEEE Transactions on 47(7): 911-918.
Wessberg, J. and M. A. Nicolelis (2004). "Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys." J Cogn Neurosci 16(6): 1022-1035.
Wessberg, J., C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach, J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan and M. A. Nicolelis (2000). "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates." Nature 408(6810): 361-365.
Wilson, B. S. and M. F. Dorman (2008). "Cochlear implants: a remarkable past and a brilliant future." Hear Res 242(1-2): 3-21.
Wilson, M. A. and B. L. McNaughton (1993). "Dynamics of the hippocampal ensemble code for space." Science 261(5124): 1055-1058.
Wolpert, D. M. and Z. Ghahramani (2000). "Computational principles of movement neuroscience." nature neuroscience 3: 1212-1217.
Wu, W., A. Shaikhouni, J. P. Donoghue and M. J. Black (2004). "Closed-loop neural control of cursor motion using a Kalman filter." Conf Proc IEEE Eng Med Biol Soc 6: 4126-4129.
Xu, Z., R. Q. So, K. K. Toe, K. K. Ang and C. Guan (2014). On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE.
Yoo, S.-S., H. Kim, E. Filandrianos, S. J. Taghados and S. Park (2013). "Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains." PloS one 8(4): e60410.
Zehr, E. P. (2015). "Future Think: Cautiously Optimistic About Brain Augmentation Using Tissue Engineering and Machine Interface." Frontiers in Systems Neuroscience 9.
Zippo, A. G., P. Romanelli, N. R. Torres Martinez, G. C. Caramenti, A. L. Benabid and G. E. M. Biella (2015). "A novel wireless recording and stimulating multichannel epicortical grid for supplementing or enhancing the sensory-motor functions in monkey (Macaca fascicularis)." Frontiers in Systems Neuroscience 9(73).