andersen at caltech. these researchers have been able to produce working bcis, even using recorded signals from far fewer neurons than did nicolelis (15–30 neurons versus 50–200 neurons). john donoghue's lab at the carney institute reported training rhesus monkeys to use a bci to track visual targets on a computer screen (closed-loop bci) with or without assistance of a joystick. schwartz's group created a bci for three-dimensional tracking in virtual reality and also reproduced bci control in a robotic arm. the same group also created headlines when they demonstrated that a monkey could feed itself pieces of fruit and marshmallows using a robotic arm controlled by the animal's own brain signals. andersen's group used recordings of premovement activity from the posterior parietal cortex in their bci, including signals created when experimental animals anticipated receiving a reward. other research in addition to predicting kinematic and kinetic parameters of limb movements, bcis that predict electromyographic or electrical activity of the muscles of primates are being developed. such bcis could be used to restore mobility in paralyzed limbs by electrically stimulating muscles. miguel nicolelis and colleagues demonstrated that the activity of large neural ensembles can predict arm position. this work made possible creation of bcis that read arm movement intentions and translate them into movements of artificial actuators. carmena and colleagues programmed the neural coding in a bci that allowed a monkey to control reaching and grasping movements by a robotic arm. lebedev and colleagues argued that brain networks reorganize to create a new representation of the robotic appendage in addition to the representation of the animal's own limbs. in 2019, researchers from ucsf published a study where they demonstrated a bci that had the potential to help patients with speech impairment caused by neurological disorders. their bci used high-density electrocorticography to tap neural activity from a patient's brain and used deep learning methods to synthesize speech. in 2021, researchers from the same group published a study showing the potential of a bci to decode words and sentences in an anarthric patient who had been unable to speak for over 15 years. the biggest impediment to bci technology at present is the lack of a sensor modality that provides safe, accurate and robust access to brain signals. it is conceivable or even likely, however, that such a sensor will be developed within the next twenty years. the use of such a sensor should greatly expand the range of communication functions that can be provided using a bci. development and implementation of a bci system is complex and time-consuming. in response to this problem, gerwin schalk has been developing a general-purpose system for bci research, called bci2000. bci2000 has been in development since 2000 in a project led by the brain–computer interface r&d program at the wadsworth center of the new york state department of health in albany, new york, united states. a new 'wireless' approach uses light-gated ion channels such as channelrhodopsin to control the activity of genetically defined subsets of neurons in vivo. in the context of a simple learning task, illumination of transfected cells in the somatosensory cortex influenced the decision-making process of freely moving mice. the use of bmis has also led to a deeper understanding of neural networks and the central nervous system. research has shown that despite the inclination of neuroscientists to believe that neurons have the most effect when working together, single neurons can be conditioned through the use of bmis to fire at a pattern that allows primates to control motor outputs. the use of bmis has led to development of the single neuron insufficiency principle which states that even with a well tuned firing rate single neurons can only carry a narrow amount of information and therefore the highest level of accuracy is achieved by recording firings of the collective ensemble. other principles discovered with the use of bmis include the neuronal multitasking principle, the neuronal mass principle, the neural degeneracy principle, and the plasticity principle. bcis are also proposed to be applied by users without disabilities. a user-centered categorization of bci approaches by thorsten o. zander and christian kothe introduces the term passive bci. next to active and reactive bci that are used for directed control, passive bcis allow for assessing and interpreting changes in the user state during human-computer interaction (hci). in a secondary, implicit control loop the computer system adapts to its user improving its usability in general. beyond bci systems that decode neural activity to drive external effectors, bci systems may be used to encode signals from the periphery. these sensory bci devices enable real-time, behaviorally-relevant decisions based upon closed-loop neural stimulation. the bci award the annual bci research award is awarded in recognition of outstanding and innovative research in the field of brain-computer interfaces. each year, a renowned research laboratory is asked to judge the submitted projects. the jury consists of world-leading bci experts recruited by the awarding laboratory. the jury selects twelve nominees, then chooses a first, second, and third-place winner, who receive awards of $3,000, $2,000, and $1,000, respectively. human bci research invasive bcis invasive bci requires surgery to implant electrodes under scalp for communicating brain signals. the main advantage is to provide more accurate reading; however, its downside includes side effects from the surgery. after the surgery, scar tissues may form which can make brain signals weaker. in addition, according to the research of abdulkader et al., (2015), the body may not accept the implanted electrodes and this can cause a medical condition. vision invasive bci research has targeted repairing damaged sight and providing new functionality for people with paralysis. invasive bcis are implanted directly into the grey matter of the brain during neurosurgery. because they lie in the grey matter, invasive devices produce the highest quality signals of bci devices but are prone to scar-tissue build-up, causing the signal to become weaker, or even non-existent, as the body reacts to a foreign object in the brain. in vision science, direct brain implants have been used to treat non-congenital (acquired) blindness. one of the first scientists to produce a working brain interface to restore sight was private researcher william dobelle. dobelle's first prototype was implanted into "jerry", a man blinded in adulthood, in 1978. a single-array bci containing 68 electrodes was implanted onto jerry's visual cortex and succeeded in producing phosphenes, the sensation of seeing light. the system included cameras mounted on glasses to send signals to the implant. initially, the implant allowed jerry to see shades of grey in a limited field of vision at a low frame-rate. this also required him to be hooked up to a mainframe computer, but shrinking electronics and faster computers made his artificial eye more portable and now enable him to perform simple tasks unassisted. dummy unit illustrating the design of a braingate interface in 2002, jens naumann, also blinded in adulthood, became the first in a series of 16 paying patients to receive dobelle's second generation implant, marking one of the earliest commercial uses of bcis. the second generation device used a more sophisticated implant enabling better mapping of phosphenes into coherent vision. phosphenes are spread out across the visual field in what researchers call "the starry-night effect". immediately after his implant, jens was able to use his imperfectly restored vision to drive an automobile slowly around the parking area of the research institute. unfortunately, dobelle died in 2004 before his processes and developments were documented. subsequently, when mr. naumann and the other patients in the program began having problems with their vision, there was no relief and they eventually lost their "sight" again. naumann wrote about his experience with dobelle's work in search for paradise: a patient's account of the artificial vision experiment and has returned to his farm in southeast ontario, canada, to resume his normal activities. movement bcis focusing on motor neuroprosthetics aim to either restore movement in individuals with paralysis or provide devices to assist them, such as interfaces with computers or robot arms. researchers at emory university in atlanta, led by philip kennedy and roy bakay, were first to install a brain implant in a human that produced signals of high enough quality to simulate movement. their patient, johnny ray (1944–2002), developed 'locked-in syndrome' after having a brain-stem stroke in 1997. ray's implant was installed in 1998 and he lived long enough to start working with the implant, eventually learning to control a computer cursor; he died in 2002 of a brain aneurysm. tetraplegic matt nagle became the first person to control an artificial hand using a bci in 2005 as part of the first nine-month human trial of cyberkinetics's braingate chip-implant. implanted in nagle's right precentral gyrus (area of the motor cortex for arm movement), the 96-electrode braingate implant allowed nagle to control a robotic arm by thinking about moving his hand as well as a computer cursor, lights and tv. one year later, professor jonathan wolpaw received the prize of the altran foundation for innovation to develop a brain computer interface with electrodes located on the surface of the skull, instead of directly in the brain. more recently, research teams led by the braingate group at brown university and a group led by university of pittsburgh medical center, both in collaborations with the united states department of veterans affairs, have demonstrated further success in direct control of robotic prosthetic limbs with many degrees of freedom using direct connections to arrays of neurons in the motor cortex of patients with tetraplegia. communication in may 2021, a stanford university team reported a successful proof-of-concept test that enabled a quadraplegic participant to input english sentences at about 86 characters per minute and 18 words per minute. the participant imagined moving his hand to write letters, and the system performed handwriting recognition on electrical signals detected in the motor cortex, utilizing hidden markov models and recurrent neural networks for decoding. a report published in july 2021 reported a paralyzed patient was able to communicate 15 words per minute using a brain implant that analyzed motor neurons that previously controlled the vocal tract. in a recent review article, researchers raised an open question of whether human information transfer rates can surpass that of language with bcis. given that recent language research has demonstrated that human information transfer rates are relatively constant across many languages, there may exist a limit at the level of information processing in the brain. on the contrary, this "upper limit" of information transfer rate may be intrinsic to language itself, as a modality for information transfer. technical challenges there exist a number of technical challenges to recording brain activity with invasive bcis. advances in cmos technology are pushing and enabling integrated, invasive bci designs with smaller size, lower power requirements, and higher signal acquisition capabilities. invasive bcis involve electrodes that penetrate brain tissue in an attempt to record action potential signals (also known as spikes) from individual, or small groups of, neurons near the electrode. the interface between a recording electrode and the electrolytic solution surrounding neurons has been modelled using the hodgkin-huxley model. electronic limitations to invasive bcis have been an active area of research in recent decades. while intracellular recordings of neurons reveal action potential voltages on the scale of hundreds of millivolts, chronic invasive bcis rely on recording extracellular voltages which typically are three orders of magnitude smaller, existing at hundreds of microvolts. further adding to the challenge of detecting signals on the scale of microvolts is the fact that the electrode-tissue interface has a high capacitance at small voltages. due to the nature of these small signals, for bci systems that incorporate functionality onto an integrated circuit, each electrode requires its own amplifier and adc, which convert analog extracellular voltages into digital signals. because a typical neuron action potential lasts for one millisecond, bcis measuring spikes must have sampling rates ranging from 300 hz to 5 khz. yet another concern is that invasive bcis must be low-power, so as to dissipate less heat to surrounding tissue; at the most basic level more power is traditionally needed to optimize signal-to-noise ratio. optimal battery design is an active area of research in bcis.illustration of invasive and partially invasive bcis: electrocorticography (ecog), endovascular, and intracortical microelectrode.challenges existing in the area of material science are central to the design of invasive