brain–computer interface brain–computer interface a brain–computer interface (bci), sometimes called a brain–machine interface (bmi) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. bcis are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. implementations of bcis range from non-invasive (eeg, meg, eog, mri) and partially invasive (ecog and endovascular) to invasive (microelectrode array), based on how close electrodes get to brain tissue. neuropsychology topics brain regions clinical neuropsychology cognitive neuropsychology cognitive neuroscience dementia human brain neuroanatomy neurophysiology neuropsychological assessment neuropsychological rehabilitation traumatic brain injury brain functions arousal attention consciousness decision making executive functions natural language learning memory motor coordination perception planning problem solving thought people alan baddeley arthur l. benton david bohm antonio damasio phineas gage norman geschwind elkhonon goldberg patricia goldman-rakic donald o. hebb kenneth heilman eric kandel edith kaplan muriel lezak benjamin libet rodolfo llinás alexander luria brenda milner karl h. pribram pasko rakic oliver sacks mark rosenzweig roger w. sperry hans-lukas teuber henry molaison ("h.m.", patient) k.c. (patient) tests benton visual retention test continuous performance task halstead-reitan neuropsychological battery hayling and brixton tests lexical decision task luria-nebraska neuropsychological battery mini–mental state examination rey–osterrieth complex figure stroop test wechsler adult intelligence scale wechsler memory scale wisconsin card sorting task psychology portal philosophy portal medicine portal research on bcis began in the 1970s by jacques vidal at the university of california, los angeles (ucla) under a grant from the national science foundation, followed by a contract from darpa. vidal's 1973 paper marks the first appearance of the expression brain–computer interface in scientific literature. due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. recently, studies in human-computer interaction via the application of machine learning to statistical temporal features extracted from the frontal lobe (eeg brainwave) data has had high levels of success in classifying mental states (relaxed, neutral, concentrating), mental emotional states (negative, neutral, positive), and thalamocortical dysrhythmia. history the history of brain–computer interfaces (bcis) starts with hans berger's discovery of the electrical activity of the human brain and the development of electroencephalography (eeg). in 1924 berger was the first to record human brain activity by means of eeg. berger was able to identify oscillatory activity, such as berger's wave or the alpha wave (8–13 hz), by analyzing eeg traces. berger's first recording device was very rudimentary. he inserted silver wires under the scalps of his patients. these were later replaced by silver foils attached to the patient's head by rubber bandages. berger connected these sensors to a lippmann capillary electrometer, with disappointing results. however, more sophisticated measuring devices, such as the siemens double-coil recording galvanometer, which displayed electric voltages as small as one ten thousandth of a volt, led to success. berger analyzed the interrelation of alternations in his eeg wave diagrams with brain diseases. eegs permitted completely new possibilities for the research of human brain activities. although the term had not yet been coined, one of the earliest examples of a working brain-machine interface was the piece music for solo performer (1965) by the american composer alvin lucier. the piece makes use of eeg and analog signal processing hardware (filters, amplifiers, and a mixing board) to stimulate acoustic percussion instruments. to perform the piece one must produce alpha waves and thereby "play" the various percussion instruments via loudspeakers which are placed near or directly on the instruments themselves. ucla professor jacques vidal coined the term "bci" and produced the first peer-reviewed publications on this topic. vidal is widely recognized as the inventor of bcis in the bci community, as reflected in numerous peer-reviewed articles reviewing and discussing the field (e.g.,). a review pointed out that vidal's 1973 paper stated the "bci challenge" of controlling external objects using eeg signals, and especially use of contingent negative variation (cnv) potential as a challenge for bci control. the 1977 experiment vidal described was the first application of bci after his 1973 bci challenge. it was a noninvasive eeg (actually visual evoked potentials (vep)) control of a cursor-like graphical object on a computer screen. the demonstration was movement in a maze. after his early contributions, vidal was not active in bci research, nor bci events such as conferences, for many years. in 2011, however, he gave a lecture in graz, austria, supported by the future bnci project, presenting the first bci, which earned a standing ovation. vidal was joined by his wife, laryce vidal, who previously worked with him at ucla on his first bci project. in 1988, a report was given on noninvasive eeg control of a physical object, a robot. the experiment described was eeg control of multiple start-stop-restart of the robot movement, along an arbitrary trajectory defined by a line drawn on a floor. the line-following behavior was the default robot behavior, utilizing autonomous intelligence and autonomous source of energy. this 1988 report written by stevo bozinovski, mihail sestakov, and liljana bozinovska was the first one about a robot control using eeg. in 1990, a report was given on a closed loop, bidirectional adaptive bci controlling computer buzzer by an anticipatory brain potential, the contingent negative variation (cnv) potential. the experiment described how an expectation state of the brain, manifested by cnv, controls in a feedback loop the s2 buzzer in the s1-s2-cnv paradigm. the obtained cognitive wave representing the expectation learning in the brain is named electroexpectogram (exg). the cnv brain potential was part of the bci challenge presented by vidal in his 1973 paper. studies in 2010s suggested the potential ability of neural stimulation to restore functional connectively and associated behaviors through modulation of molecular mechanisms of synaptic efficacy. this opened the door for the concept that bci technologies may be able to restore function in addition to enabling functionality. since 2013, darpa has funded bci technology through the brain initiative, which has supported work out of the university of pittsburgh medical center, paradromics, brown, and synchron, among others. bcis versus neuroprosthetics neuroprosthetics is an area of neuroscience concerned with neural prostheses, that is, using artificial devices to replace the function of impaired nervous systems and brain-related problems, or of sensory organs or organs itself (bladder, diaphragm, etc.). as of december 2010, cochlear implants had been implanted as neuroprosthetic device in approximately 220,000 people worldwide. there are also several neuroprosthetic devices that aim to restore vision, including retinal implants. the first neuroprosthetic device, however, was the pacemaker. the terms are sometimes used interchangeably. neuroprosthetics and bcis seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function. both use similar experimental methods and surgical techniques. animal bci research several laboratories have managed to record signals from monkey and rat cerebral cortices to operate bcis to produce movement. monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output. in may 2008 photographs that showed a monkey at the university of pittsburgh medical center operating a robotic arm by thinking were published in a number of well-known science journals and magazines. sheep too have been used to evaluate bci technology including synchron's stentrode. in 2020, elon musk's neuralink was successfully implanted in a pig, announced in a widely viewed webcast. in 2021, elon musk announced that he had successfully enabled a monkey to play video games using neuralink's device. early work monkey operating a robotic arm with brain–computer interfacing (schwartz lab, university of pittsburgh) in 1969 the operant conditioning studies of fetz and colleagues, at the regional primate research center and department of physiology and biophysics, university of washington school of medicine in seattle, showed for the first time that monkeys could learn to control the deflection of a biofeedback meter arm with neural activity. similar work in the 1970s established that monkeys could quickly learn to voluntarily control the firing rates of individual and multiple neurons in the primary motor cortex if they were rewarded for generating appropriate patterns of neural activity. studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. in the 1980s, apostolos georgopoulos at johns hopkins university found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms (based on a cosine function). he also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands, but was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment. there has been rapid development in bcis since the mid-1990s. several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices. kennedy and yang dan phillip kennedy (who later founded neural signals in 1987) and colleagues built the first intracortical brain–computer interface by implanting neurotrophic-cone electrodes into monkeys. yang dan and colleagues' recordings of cat vision using a bci implanted in the lateral geniculate nucleus (top row: original image; bottom row: recording) in 1999, researchers led by yang dan at the university of california, berkeley decoded neuronal firings to reproduce images seen by cats. the team used an array of electrodes embedded in the thalamus (which integrates all of the brain's sensory input) of sharp-eyed cats. researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. the cats were shown eight short movies, and their neuron firings were recorded. using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects. similar results in humans have since been achieved by researchers in japan (see below). nicolelis miguel nicolelis, a professor at duke university, in durham, north carolina, has been a prominent proponent of using multiple electrodes spread over a greater area of the brain to obtain neuronal signals to drive a bci. after conducting initial studies in rats during the 1990s, nicolelis and his colleagues developed bcis that decoded brain activity in owl monkeys and used the devices to reproduce monkey movements in robotic arms. monkeys have advanced reaching and grasping abilities and good hand manipulation skills, making them ideal test subjects for this kind of work. by 2000, the group succeeded in building a bci that reproduced owl monkey movements while the monkey operated a joystick or reached for food. the bci operated in real time and could also control a separate robot remotely over internet protocol. but the monkeys could not see the arm moving and did not receive any feedback, a so-called open-loop bci. diagram of the bci developed by miguel nicolelis and colleagues for use on rhesus monkeys later experiments by nicolelis using rhesus monkeys succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm. with their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human neurophysiology than owl monkeys. the monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden. the monkeys were later shown the robot directly and learned to control it by viewing its movements. the bci used velocity predictions to control reaching movements and simultaneously predicted handgripping force. in 2011 o'doherty and colleagues showed a bci with sensory feedback with rhesus monkeys. the monkey was brain controlling the position of an avatar arm while receiving sensory feedback through direct intracortical stimulation (icms) in the arm representation area of the sensory cortex. donoghue, schwartz and andersen bcis are a core focus of the carney institute for brain science at brown university. other laboratories which have developed bcis and algorithms that decode neuron signals include the carney institute for brain science at brown university and the labs of andrew schwartz at the university of pittsburgh and richard