bcis. variations in signal quality over time have been commonly observed with implantable microelectrodes. optimal material and mechanical characteristics for long term signal stability in invasive bcis has been an active area of research. it has been proposed that the formation of glial scarring, secondary to damage at the electrode-tissue interface, is likely responsible for electrode failure and reduced recording performance. research has suggested that blood-brain barrier leakage, either at the time of insertion or over time, may be responsible for the inflammatory and glial reaction to chronic microelectrodes implanted in the brain. as a result, flexible and tissue-like designs have been researched and developed to minimize foreign-body reaction by means of matching the young's modulus of the electrode closer to that of brain tissue. partially invasive bcis partially invasive bci devices are implanted inside the skull but rest outside the brain rather than within the grey matter. they produce better resolution signals than non-invasive bcis where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar-tissue in the brain than fully invasive bcis. there has been preclinical demonstration of intracortical bcis from the stroke perilesional cortex. endovascular a systematic review published in 2020 detailed multiple studies, both clinical and non-clinical, dating back decades investigating the feasibility of endovascular bcis. in recent years, the biggest advance in partially invasive bcis has emerged in the area of interventional neurology. in 2010, researchers affiliated with university of melbourne had begun developing a bci that could be inserted via the vascular system. the australian neurologist thomas oxley (mount sinai hospital) conceived the idea for this bci, called stentrode, which has received funding from darpa. preclinical studies evaluated the technology in sheep. the stentrode, a monolithic stent electrode array, is designed to be delivered via an intravenous catheter under image-guidance to the superior sagittal sinus, in the region which lies adjacent to motor cortex. this proximity to motor cortex underlies the stentrode's ability to measure neural activity. the procedure is most similar to how venous sinus stents are placed for the treatment of idiopathic intracranial hypertension. the stentrode communicates neural activity to a battery-less telemetry unit implanted in the chest, which communicates wirelessly with an external telemetry unit capable of power and data transfer. while an endovascular bci benefits from avoiding craniotomy for insertion, risks such as clotting and venous thrombosis are possible. first-in-human trials with the stentrode are underway. in november 2020, two participants with amyotrophic lateral sclerosis were able to wirelessly control an operating system to text, email, shop, and bank using direct thought through the stentrode brain-computer interface, marking the first time a brain-computer interface was implanted via the patient's blood vessels, eliminating the need for open brain surgery. in january 2023, researchers reported no serious adverse events during the first year for all four patients who could use it to operate computers. ecog electrocorticography (ecog) measures the electrical activity of the brain taken from beneath the skull in a similar way to non-invasive electroencephalography, but the electrodes are embedded in a thin plastic pad that is placed above the cortex, beneath the dura mater. ecog technologies were first trialled in humans in 2004 by eric leuthardt and daniel moran from washington university in st. louis. in a later trial, the researchers enabled a teenage boy to play space invaders using his ecog implant. this research indicates that control is rapid, requires minimal training, and may be an ideal tradeoff with regards to signal fidelity and level of invasiveness. signals can be either subdural or epidural, but are not taken from within the brain parenchyma itself. it has not been studied extensively until recently due to the limited access of subjects. currently, the only manner to acquire the signal for study is through the use of patients requiring invasive monitoring for localization and resection of an epileptogenic focus. ecog is a very promising intermediate bci modality because it has higher spatial resolution, better signal-to-noise ratio, wider frequency range, and less training requirements than scalp-recorded eeg, and at the same time has lower technical difficulty, lower clinical risk, and may have superior long-term stability than intracortical single-neuron recording. this feature profile and recent evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities. light reactive imaging bci devices are still in the realm of theory. recent work published by edward chang and joseph makin from ucsf revealed that ecog signals could be used to decode speech from epilepsy patients implanted with high-density ecog arrays over the peri-sylvian cortices. their study achieved word error rates of 3% (a marked improvement from prior publications) utilizing an encoder-decoder neural network, which translated ecog data into one of fifty sentences composed of 250 unique words. non-invasive bcis there have also been experiments in humans using non-invasive neuroimaging technologies as interfaces. the substantial majority of published bci work involves noninvasive eeg-based bcis. noninvasive eeg-based technologies and interfaces have been used for a much broader variety of applications. although eeg-based interfaces are easy to wear and do not require surgery, they have relatively poor spatial resolution and cannot effectively use higher-frequency signals because the skull dampens signals, dispersing and blurring the electromagnetic waves created by the neurons. eeg-based interfaces also require some time and effort prior to each usage session, whereas non-eeg-based ones, as well as invasive ones require no prior-usage training. overall, the best bci for each user depends on numerous factors. electrooculography (eog) in 1989, a report was given on control of a mobile robot by eye movement using electrooculography (eog) signals. a mobile robot was driven from a start to a goal point using five eog commands, interpreted as forward, backward, left, right, and stop. the eog as a challenge of controlling external objects was presented by vidal in his 1973 paper. pupil-size oscillation a 2016 article described an entirely new communication device and non-eeg-based human-computer interface, which requires no visual fixation, or ability to move the eyes at all. the interface is based on covert interest; directing one's attention to a chosen letter on a virtual keyboard, without the need to move one's eyes to look directly at the letter. each letter has its own (background) circle which micro-oscillates in brightness differently from all of the other letters. the letter selection is based on best fit between unintentional pupil-size oscillation and the background circle's brightness oscillation pattern. accuracy is additionally improved by the user's mental rehearsing of the words 'bright' and 'dark' in synchrony with the brightness transitions of the letter's circle. functional near-infrared spectroscopy in 2014 and 2017, a bci using functional near-infrared spectroscopy for "locked-in" patients with amyotrophic lateral sclerosis (als) was able to restore some basic ability of the patients to communicate with other people. electroencephalography (eeg)-based brain-computer interfaces recordings of brainwaves produced by an electroencephalogram after the bci challenge was stated by vidal in 1973, the initial reports on non-invasive approach included control of a cursor in 2d using vep (vidal 1977), control of a buzzer using cnv (bozinovska et al. 1988, 1990), control of a physical object, a robot, using a brain rhythm (alpha) (bozinovski et al. 1988), control of a text written on a screen using p300 (farwell and donchin, 1988). in the early days of bci research, another substantial barrier to using electroencephalography (eeg) as a brain–computer interface was the extensive training required before users can work the technology. for example, in experiments beginning in the mid-1990s, niels birbaumer at the university of tübingen in germany trained severely paralysed people to self-regulate the slow cortical potentials in their eeg to such an extent that these signals could be used as a binary signal to control a computer cursor. (birbaumer had earlier trained epileptics to prevent impending fits by controlling this low voltage wave.) the experiment saw ten patients trained to move a computer cursor by controlling their brainwaves. the process was slow, requiring more than an hour for patients to write 100 characters with the cursor, while training often took many months. however, the slow cortical potential approach to bcis has not been used in several years, since other approaches require little or no training, are faster and more accurate, and work for a greater proportion of users. another research parameter is the type of oscillatory activity that is measured. gert pfurtscheller founded the bci lab 1991 and fed his research results on motor imagery in the first online bci based on oscillatory features and classifiers. together with birbaumer and jonathan wolpaw at new york state university they focused on developing technology that would allow users to choose the brain signals they found easiest to operate a bci, including mu and beta rhythms. a further parameter is the method of feedback used and this is shown in studies of p300 signals. patterns of p300 waves are generated involuntarily (stimulus-feedback) when people see something they recognize and may allow bcis to decode categories of thoughts without training patients first. by contrast, the biofeedback methods described above require learning to control brainwaves so the resulting brain activity can be detected. in 2005 it was reported research on eeg emulation of digital control circuits for bci, with example of a cnv flip-flop. in 2009 it was reported noninvasive eeg control of a robotic arm using a cnv flip-flop. in 2011 it was reported control of two robotic arms solving tower of hanoi task with three disks using a cnv flip-flop. in 2015 it was described eeg-emulation of a schmitt trigger, flip-flop, demultiplexer, and modem. while an eeg based brain-computer interface has been pursued extensively by a number of research labs, recent advancements made by bin he and his team at the university of minnesota suggest the potential of an eeg based brain-computer interface to accomplish tasks close to invasive brain-computer interface. using advanced functional neuroimaging including bold functional mri and eeg source imaging, bin he and co-workers identified the co-variation and co-localization of electrophysiological and hemodynamic signals induced by motor imagination. refined by a neuroimaging approach and by a training protocol, bin he and co-workers demonstrated the ability of a non-invasive eeg based brain-computer interface to control the flight of a virtual helicopter in 3-dimensional space, based upon motor imagination. in june 2013 it was announced that bin he had developed the technique to enable a remote-control helicopter to be guided through an obstacle course. in addition to a brain-computer interface based on brain waves, as recorded from scalp eeg electrodes, bin he and co-workers explored a virtual eeg signal-based brain-computer interface by first solving the eeg inverse problem and then used the resulting virtual eeg for brain-computer interface tasks. well-controlled studies suggested the merits of such a source analysis based brain-computer interface. a 2014 study found that severely motor-impaired patients could communicate faster and more reliably with non-invasive eeg bci, than with any muscle-based communication channel. a 2016 study found that the emotiv epoc device may be more suitable for control tasks using the attention/meditation level or eye blinking than the neurosky mindwave device. a 2019 study found that the application of evolutionary algorithms could improve eeg mental state classification with a non-invasive muse device, enabling high quality classification of data acquired by a cheap consumer-grade eeg sensing device. in a 2021 systematic review of randomized controlled trials using bci for upper-limb rehabilitation after stroke, eeg-based bci was found to have significant efficacy in improving upper-limb motor function compared to control therapies. more specifically, bci studies that utilized band power features, motor imagery, and functional electrical stimulation in their design were found to be more efficacious than alternatives. another 2021 systematic review focused on robotic-assisted eeg-based bci for hand rehabilitation after stroke. improvement in motor assessment scores was observed in three of eleven studies included in the systematic review. dry active electrode arrays in the early 1990s babak taheri, at university of california, davis demonstrated the first single and also multichannel dry active electrode arrays using micro-machining. the single channel dry eeg electrode construction and results were published in 1994. the arrayed electrode was also demonstrated to perform well compared to silver/silver chloride electrodes. the device consisted of four sites of sensors with integrated electronics to reduce noise by impedance matching. the advantages of such electrodes are: