(1) no electrolyte used, (2) no skin preparation, (3) significantly reduced sensor size, and (4) compatibility with eeg monitoring systems. the active electrode array is an integrated system made of an array of capacitive sensors with local integrated circuitry housed in a package with batteries to power the circuitry. this level of integration was required to achieve the functional performance obtained by the electrode. the electrode was tested on an electrical test bench and on human subjects in four modalities of eeg activity, namely: (1) spontaneous eeg, (2) sensory event-related potentials, (3) brain stem potentials, and (4) cognitive event-related potentials. the performance of the dry electrode compared favorably with that of the standard wet electrodes in terms of skin preparation, no gel requirements (dry), and higher signal-to-noise ratio. in 1999 researchers at case western reserve university, in cleveland, ohio, led by hunter peckham, used 64-electrode eeg skullcap to return limited hand movements to quadriplegic jim jatich. as jatich concentrated on simple but opposite concepts like up and down, his beta-rhythm eeg output was analysed using software to identify patterns in the noise. a basic pattern was identified and used to control a switch: above average activity was set to on, below average off. as well as enabling jatich to control a computer cursor the signals were also used to drive the nerve controllers embedded in his hands, restoring some movement. ssvep mobile eeg bcis in 2009, the nctu brain-computer-interface-headband was reported. the researchers who developed this bci-headband also engineered silicon-based microelectro-mechanical system (mems) dry electrodes designed for application in non-hairy sites of the body. these electrodes were secured to the daq board in the headband with snap-on electrode holders. the signal processing module measured alpha activity and the bluetooth enabled phone assessed the patients' alertness and capacity for cognitive performance. when the subject became drowsy, the phone sent arousing feedback to the operator to rouse them. this research was supported by the national science council, taiwan, r.o.c., nsc, national chiao-tung university, taiwan's ministry of education, and the u.s. army research laboratory. in 2011, researchers reported a cellular based bci with the capability of taking eeg data and converting it into a command to cause the phone to ring. this research was supported in part by abraxis bioscience llp, the u.s. army research laboratory, and the army research office. the developed technology was a wearable system composed of a four channel bio-signal acquisition/amplification module, a wireless transmission module, and a bluetooth enabled cell phone. the electrodes were placed so that they pick up steady state visual evoked potentials (ssveps). ssveps are electrical responses to flickering visual stimuli with repetition rates over 6 hz that are best found in the parietal and occipital scalp regions of the visual cortex. it was reported that with this bci setup, all study participants were able to initiate the phone call with minimal practice in natural environments. the scientists claim that their studies using a single channel fast fourier transform (fft) and multiple channel system canonical correlation analysis (cca) algorithm support the capacity of mobile bcis. the cca algorithm has been applied in other experiments investigating bcis with claimed high performance in accuracy as well as speed. while the cellular based bci technology was developed to initiate a phone call from ssveps, the researchers said that it can be translated for other applications, such as picking up sensorimotor mu/beta rhythms to function as a motor-imagery based bci. in 2013, comparative tests were performed on android cell phone, tablet, and computer based bcis, analyzing the power spectrum density of resultant eeg ssveps. the stated goals of this study, which involved scientists supported in part by the u.s. army research laboratory, were to "increase the practicability, portability, and ubiquity of an ssvep-based bci, for daily use". citation it was reported that the stimulation frequency on all mediums was accurate, although the cell phone's signal demonstrated some instability. the amplitudes of the ssveps for the laptop and tablet were also reported to be larger than those of the cell phone. these two qualitative characterizations were suggested as indicators of the feasibility of using a mobile stimulus bci. limitations in 2011, researchers stated that continued work should address ease of use, performance robustness, reducing hardware and software costs. one of the difficulties with eeg readings is the large susceptibility to motion artifacts. in most of the previously described research projects, the participants were asked to sit still, reducing head and eye movements as much as possible, and measurements were taken in a laboratory setting. however, since the emphasized application of these initiatives had been in creating a mobile device for daily use, the technology had to be tested in motion. in 2013, researchers tested mobile eeg-based bci technology, measuring ssveps from participants as they walked on a treadmill at varying speeds. this research was supported by the office of naval research, army research office, and the u.s. army research laboratory. stated results were that as speed increased the ssvep detectability using cca decreased. as independent component analysis (ica) had been shown to be efficient in separating eeg signals from noise, the scientists applied ica to cca extracted eeg data. they stated that the cca data with and without ica processing were similar. thus, they concluded that cca independently demonstrated a robustness to motion artifacts that indicates it may be a beneficial algorithm to apply to bcis used in real world conditions. one of the major problems in eeg-based bci applications is the low spatial resolution. several solutions have been suggested to address this issue since 2019, which include: eeg source connectivity based on graph theory, eeg pattern recognition based on topomap, eeg-fmri fusion, and so on. prosthesis and environment control non-invasive bcis have also been applied to enable brain-control of prosthetic upper and lower extremity devices in people with paralysis. for example, gert pfurtscheller of graz university of technology and colleagues demonstrated a bci-controlled functional electrical stimulation system to restore upper extremity movements in a person with tetraplegia due to spinal cord injury. between 2012 and 2013, researchers at the university of california, irvine demonstrated for the first time that it is possible to use bci technology to restore brain-controlled walking after spinal cord injury. in their spinal cord injury research study, a person with paraplegia was able to operate a bci-robotic gait orthosis to regain basic brain-controlled ambulation. in 2009 alex blainey, an independent researcher based in the uk, successfully used the emotiv epoc to control a 5 axis robot arm. he then went on to make several demonstration mind controlled wheelchairs and home automation that could be operated by people with limited or no motor control such as those with paraplegia and cerebral palsy. research into military use of bcis funded by darpa has been ongoing since the 1970s. the current focus of research is user-to-user communication through analysis of neural signals. diy and open source bci in 2001, the openeeg project was initiated by a group of diy neuroscientists and engineers. the modulareeg was the primary device created by the openeeg community; it was a 6-channel signal capture board that cost between $200 and $400 to make at home. the openeeg project marked a significant moment in the emergence of diy brain-computer interfacing. in 2010, the frontier nerds of nyu's itp program published a thorough tutorial titled how to hack toy eegs. the tutorial, which stirred the minds of many budding diy bci enthusiasts, demonstrated how to create a single channel at-home eeg with an arduino and a mattel mindflex at a very reasonable price. this tutorial amplified the diy bci movement. meg and mri main articles: magnetoencephalography and magnetic resonance imaging atr labs' reconstruction of human vision using fmri (top row: original image; bottom row: reconstruction from mean of combined readings) magnetoencephalography (meg) and functional magnetic resonance imaging (fmri) have both been used successfully as non-invasive bcis. in a widely reported experiment, fmri allowed two users being scanned to play pong in real-time by altering their haemodynamic response or brain blood flow through biofeedback techniques. fmri measurements of haemodynamic responses in real time have also been used to control robot arms with a seven-second delay between thought and movement. in 2008 research developed in the advanced telecommunications research (atr) computational neuroscience laboratories in kyoto, japan, allowed the scientists to reconstruct images directly from the brain and display them on a computer in black and white at a resolution of 10x10 pixels. the article announcing these achievements was the cover story of the journal neuron of 10 december 2008. in 2011 researchers from uc berkeley published a study reporting second-by-second reconstruction of videos watched by the study's subjects, from fmri data. this was achieved by creating a statistical model relating visual patterns in videos shown to the subjects, to the brain activity caused by watching the videos. this model was then used to look up the 100 one-second video segments, in a database of 18 million seconds of random youtube videos, whose visual patterns most closely matched the brain activity recorded when subjects watched a new video. these 100 one-second video extracts were then combined into a mashed-up image that resembled the video being watched. motor imagery motor imagery involves the imagination of the movement of various body parts resulting in sensorimotor cortex activation, which modulates sensorimotor oscillations in the eeg. this can be detected by the bci to infer a user's intent. motor imagery typically requires a number of sessions of training before acceptable control of the bci is acquired. these training sessions may take a number of hours over several days before users can consistently employ the technique with acceptable levels of precision. regardless of the duration of the training session, users are unable to master the control scheme. this results in very slow pace of the gameplay. advanced machine learning methods were recently developed to compute a subject-specific model for detecting the performance of motor imagery. the top performing algorithm from bci competition iv dataset 2 for motor imagery is the filter bank common spatial pattern, developed by ang et al. from a*star, singapore. bio/neurofeedback for passive bci designs biofeedback is used to monitor a subject's mental relaxation. in some cases, biofeedback does not monitor electroencephalography (eeg), but instead bodily parameters such as electromyography (emg), galvanic skin resistance (gsr), and heart rate variability (hrv). many biofeedback systems are used to treat certain disorders such as attention deficit hyperactivity disorder (adhd), sleep problems in children, teeth grinding, and chronic pain. eeg biofeedback systems typically monitor four different bands (theta: 4–7 hz, alpha:8–12 hz, smr: 12–15 hz, beta: 15–18 hz) and challenge the subject to control them. passive bci involves using bci to enrich human–machine interaction with implicit information on the actual user's state, for example, simulations to detect when users intend to push brakes during an emergency car stopping procedure. game developers using passive bcis need to acknowledge that through repetition of game levels the user's cognitive state will change or adapt. within the first play of a level, the user will react to things differently from during the second play: for example, the user will be less surprised at an event in the game if they are expecting it. visual evoked potential (vep) a vep is an electrical potential recorded after a subject is presented with a type of visual stimuli. there are several types of veps. steady-state visually evoked potentials (ssveps) use potentials generated by exciting the retina, using visual stimuli modulated at certain frequencies. ssvep's stimuli are often formed from alternating checkerboard patterns and at times simply use flashing images. the frequency of the phase reversal of the stimulus used can be clearly distinguished in the spectrum of an eeg; this makes detection of ssvep stimuli relatively easy. ssvep has proved to be successful within many bci systems. this is due to several factors, the signal elicited is measurable in as large a population as the transient vep and blink movement and electrocardiographic artefacts do not affect the frequencies monitored. in addition, the ssvep signal is exceptionally robust; the topographic organization of the primary visual cortex is such that a broader area obtains afferents from the central or fovial region of the visual field. ssvep does have several problems however. as ssveps use flashing stimuli to infer a user's intent,