Nikiya Anton Bettey 2021: 02-12-2023-0821 - CONSCIOUSNESS, CONSCIOUSNESS AFTER DEATH, NODE HOP, NETWORK WIRELESS, COMPUTERS, ETC. draft

Sunday, February 12, 2023

02-12-2023-0821 - CONSCIOUSNESS, CONSCIOUSNESS AFTER DEATH, NODE HOP, NETWORK WIRELESS, COMPUTERS, ETC. draft

https://en.wikipedia.org/wiki/Flea#Egg

https://en.wikipedia.org/wiki/Phosphor

Consciousness after death is a common theme in society and culture in the context of life after death. Scientific research has established that the physiological functioning of the brain, the cessation of which defines brain death, is closely connected to mental states. However, many believe in some form of life after death, which is a feature of many religions.

https://en.wikipedia.org/wiki/Consciousness_after_death

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https://www.sciencedaily.com/releases/2020/09/200903114214.htm

https://www.forbes.com/sites/fernandezelizabeth/2020/09/06/is-consciousness-continuous-like-a-movie-or-discrete-like-a-flipbook/?sh=76801d131013

https://www.youtube.com/watch?v=WnoIf2NwaRY

https://qz.com/663729/new-research-suggests-that-consciousness-is-developed-in-two-stages

https://elifesciences.org/articles/23871

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwir7bfix5D9AhXijIkEHSBWBzk4ChAWegQIERAB&url=https%3A%2F%2Fgrazianolab.princeton.edu%2Fdocument%2F183&usg=AOvVaw0Pyvs6Hsplapn00kx-vJd8

https://anthrosource.onlinelibrary.wiley.com/journal/15563537

https://www.sciencedirect.com/book/9780128009482/the-neurology-of-consciousness

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwi-voPgyJD9AhU0lYkEHRt8AUw4FBAWegQICRAB&url=https%3A%2F%2Fwww.pnas.org%2Fdoi%2F10.1073%2Fpnas.2024455119&usg=AOvVaw27_asx2sKtWwAvR9COFKAT

https://webspace.ship.edu/cgboer/jamesselection.html

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiJicCmyZD9AhV5lYkEHZHICng4PBAWegQICRAB&url=https%3A%2F%2Fresearchoutreach.org%2Farticles%2Fconsciousness-quantum-mechanics-plancks-constant%2F&usg=AOvVaw2K1_a7ZTCVGJdVZV_3afOI

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https://en.wikipedia.org/wiki/Biogerontology

https://en.wikipedia.org/wiki/Information-theoretic_death

https://en.wikipedia.org/wiki/Senescence

https://en.wikipedia.org/wiki/Holonomic_brain_theory

https://en.wikipedia.org/wiki/Electromagnetic_theories_of_consciousness

https://en.wikipedia.org/wiki/Artificial_consciousness

https://en.wikipedia.org/wiki/Binding_problem

https://en.wikipedia.org/wiki/Quantum_mind

https://en.wikipedia.org/wiki/Secondary_consciousness

https://en.wikipedia.org/wiki/Sentience

https://en.wikipedia.org/wiki/Soul

https://en.wikipedia.org/wiki/Stream_of_consciousness_(psychology)

https://en.wikipedia.org/wiki/Subconscious

https://en.wikipedia.org/wiki/Subjectivity

https://en.wikipedia.org/wiki/Unconscious_mind

https://en.wikipedia.org/wiki/Unconsciousness

https://en.wikipedia.org/wiki/Visual_masking

https://en.wikipedia.org/wiki/Animal_consciousness

https://en.wikipedia.org/wiki/Cartesian_theater

https://en.wikipedia.org/wiki/Subconscious

https://en.wikipedia.org/wiki/Unconscious_mind

https://en.wikipedia.org/wiki/Dual_consciousness

https://en.wikipedia.org/wiki/Divided_consciousness

https://en.wikipedia.org/wiki/Secondary_consciousness

https://en.wikipedia.org/wiki/Stream_of_consciousness_(psychology)

https://en.wikipedia.org/wiki/Category:Consciousness

https://en.wikipedia.org/wiki/Category:Devices_to_alter_consciousness

https://en.wikipedia.org/wiki/Feraliminal_Lycanthropizer

https://en.wikipedia.org/wiki/Brain_implant

https://en.wikipedia.org/wiki/Dreamachine

https://en.wikipedia.org/wiki/MKUltra

https://en.wikipedia.org/wiki/Sonic_weapon

https://en.wikipedia.org/wiki/Hypersonic_weapon

https://en.wikipedia.org/wiki/Scramjet

https://en.wikipedia.org/wiki/Category:Insurgency_weapons

https://en.wikipedia.org/wiki/Category:Space_weapons

https://en.wikipedia.org/wiki/Category:Railway_weapons

https://en.wikipedia.org/wiki/Category:Police_weapons

https://en.wikipedia.org/wiki/Category:Weapon_operation

https://en.wikipedia.org/wiki/Category:Non-lethal_weapons

https://en.wikipedia.org/wiki/Category:Energy_weapons

https://en.wikipedia.org/wiki/Category:Crew_served_weapons

https://en.wikipedia.org/wiki/Category:Flexible_weapons

https://en.wikipedia.org/wiki/Category:Fortification_weapons

https://en.wikipedia.org/wiki/Category:Weapon_guidance

https://en.wikipedia.org/wiki/Category:Guided_weapons

https://en.wikipedia.org/wiki/Category:Personal_weapons

https://en.wikipedia.org/wiki/Category:Paramilitary_weapons

https://en.wikipedia.org/wiki/Category:Mythological_weapons

https://en.wikipedia.org/wiki/Category:Weapons_countermeasures

https://en.wikipedia.org/wiki/Weaponry_(radio_program)

https://en.wikipedia.org/wiki/Psychoacoustics

https://en.wikipedia.org/wiki/Psychophysics

History

Many of the classical techniques and theories of psychophysics were formulated in 1860 when Gustav Theodor Fechner in Leipzig published Elemente der Psychophysik (Elements of Psychophysics).^[5] He coined the term "psychophysics", describing research intended to relate physical stimuli to the contents of consciousness such as sensations (Empfindungen). As a physicist and philosopher, Fechner aimed at developing a method that relates matter to the mind, connecting the publicly observable world and a person's privately experienced impression of it. His ideas were inspired by experimental results on the sense of touch and light obtained in the early 1830s by the German physiologist Ernst Heinrich Weber in Leipzig,^[6]^[7] most notably those on the minimum discernible difference in intensity of stimuli of moderate strength (just noticeable difference; jnd) which Weber had shown to be a constant fraction of the reference intensity, and which Fechner referred to as Weber's law. From this, Fechner derived his well-known logarithmic scale, now known as Fechner scale. Weber's and Fechner's work formed one of the bases of psychology as a science, with Wilhelm Wundt founding the first laboratory for psychological research in Leipzig (Institut für experimentelle Psychologie). Fechner's work systematised the introspectionist approach (psychology as the science of consciousness), that had to contend with the Behaviorist approach in which even verbal responses are as physical as the stimuli.

Detection

An absolute threshold is the level of intensity of a stimulus at which the subject is able to detect the presence of the stimulus some proportion of the time (a p level of 50% is often used).^[16] An example of an absolute threshold is the number of hairs on the back of one's hand that must be touched before it can be felt – a participant may be unable to feel a single hair being touched, but may be able to feel two or three as this exceeds the threshold. Absolute threshold is also often referred to as detection threshold. Several different methods are used for measuring absolute thresholds (as with discrimination thresholds; see below).

Discrimination

A difference threshold (or just-noticeable difference, JND) is the magnitude of the smallest difference between two stimuli of differing intensities that the participant is able to detect some proportion of the time (the percentage depending on the kind of task). To test this threshold, several different methods are used. The subject may be asked to adjust one stimulus until it is perceived as the same as the other (method of adjustment), may be asked to describe the direction and magnitude of the difference between two stimuli, or may be asked to decide whether intensities in a pair of stimuli are the same or not (forced choice). The just-noticeable difference (JND) is not a fixed quantity; rather, it depends on how intense the stimuli being measured are and the particular sense being measured.^[17] Weber's Law states that the just-noticeable difference of a stimulus is a constant proportion despite variation in intensity.^[18]

In discrimination experiments, the experimenter seeks to determine at what point the difference between two stimuli, such as two weights or two sounds, is detectable. The subject is presented with one stimulus, for example a weight, and is asked to say whether another weight is heavier or lighter (in some experiments, the subject may also say the two weights are the same). At the point of subjective equality (PSE), the subject perceives the two weights to be the same. The just-noticeable difference,^[19] or difference limen (DL), is the magnitude of the difference in stimuli that the subject notices some proportion p of the time (50% is usually used for p in the comparison task). In addition, a two-alternative forced choice (2-afc) paradigm can be used to assess the point at which performance reduces to chance on a discrimination between two alternatives (p will then typically be 75% since p=50% corresponds to chance in the 2-afc task).

Absolute and difference thresholds are sometimes considered similar in principle because there is always background noise interfering with our ability to detect stimuli.^[6]^[20]

https://en.wikipedia.org/wiki/Psychophysics

Classical psychophysical methods

Psychophysical experiments have traditionally used three methods for testing subjects' perception in stimulus detection and difference detection experiments: the method of limits, the method of constant stimuli and the method of adjustment.^[22]

Method of limits

In the ascending method of limits, some property of the stimulus starts out at a level so low that the stimulus could not be detected, then this level is gradually increased until the participant reports that they are aware of it. For example, if the experiment is testing the minimum amplitude of sound that can be detected, the sound begins too quietly to be perceived, and is made gradually louder. In the descending method of limits, this is reversed. In each case, the threshold is considered to be the level of the stimulus property at which the stimuli are just detected.^[22]

In experiments, the ascending and descending methods are used alternately and the thresholds are averaged. A possible disadvantage of these methods is that the subject may become accustomed to reporting that they perceive a stimulus and may continue reporting the same way even beyond the threshold (the error of habituation). Conversely, the subject may also anticipate that the stimulus is about to become detectable or undetectable and may make a premature judgment (the error of anticipation).

To avoid these potential pitfalls, Georg von Békésy introduced the staircase procedure in 1960 in his study of auditory perception. In this method, the sound starts out audible and gets quieter after each of the subject's responses, until the subject does not report hearing it. At that point, the sound is made louder at each step, until the subject reports hearing it, at which point it is made quieter in steps again. This way the experimenter is able to "zero in" on the threshold.^[22]

Method of constant stimuli

Instead of being presented in ascending or descending order, in the method of constant stimuli the levels of a certain property of the stimulus are not related from one trial to the next, but presented randomly. This prevents the subject from being able to predict the level of the next stimulus, and therefore reduces errors of habituation and expectation. For 'absolute thresholds' again the subject reports whether they are able to detect the stimulus.^[22] For 'difference thresholds' there has to be a constant comparison stimulus with each of the varied levels. Friedrich Hegelmaier described the method of constant stimuli in an 1852 paper.^[23] This method allows for full sampling of the psychometric function, but can result in a lot of trials when several conditions are interleaved.

Method of adjustment

In the method of adjustment, the subject is asked to control the level of the stimulus and to alter it until it is just barely detectable against the background noise, or is the same as the level of another stimulus. The adjustment is repeated many times. This is also called the method of average error.^[22] In this method, the observers themselves control the magnitude of the variable stimulus, beginning with a level that is distinctly greater or lesser than a standard one and vary it until they are satisfied by the subjective equality of the two. The difference between the variable stimuli and the standard one is recorded after each adjustment, and the error is tabulated for a considerable series. At the end, the mean is calculated giving the average error which can be taken as a measure of sensitivity.

Adaptive psychophysical methods

The classic methods of experimentation are often argued to be inefficient. This is because, in advance of testing, the psychometric threshold is usually unknown and most of the data are collected at points on the psychometric function that provide little information about the parameter of interest, usually the threshold. Adaptive staircase procedures (or the classical method of adjustment) can be used such that the points sampled are clustered around the psychometric threshold. Data points can also be spread in a slightly wider range, if the psychometric function's slope is also of interest. Adaptive methods can thus be optimized for estimating the threshold only, or both threshold and slope. Adaptive methods are classified into staircase procedures (see below) and Bayesian, or maximum-likelihood, methods. Staircase methods rely on the previous response only, and are easier to implement. Bayesian methods take the whole set of previous stimulus-response pairs into account and are generally more robust against lapses in attention.^[24] Practical examples are found here.^[21]

Staircase procedures

Diagram showing a specific staircase procedure: Transformed Up/Down Method (1 up/ 2 down rule). Until the first reversal (which is neglected) the simple up/down rule and a larger step size is used.

Staircases usually begin with a high intensity stimulus, which is easy to detect. The intensity is then reduced until the observer makes a mistake, at which point the staircase 'reverses' and intensity is increased until the observer responds correctly, triggering another reversal. The values for the last of these 'reversals' are then averaged. There are many different types of staircase procedures, using different decision and termination rules. Step-size, up/down rules and the spread of the underlying psychometric function dictate where on the psychometric function they converge.^[24] Threshold values obtained from staircases can fluctuate wildly, so care must be taken in their design. Many different staircase algorithms have been modeled and some practical recommendations suggested by Garcia-Perez.^[25]

One of the more common staircase designs (with fixed-step sizes) is the 1-up-N-down staircase. If the participant makes the correct response N times in a row, the stimulus intensity is reduced by one step size. If the participant makes an incorrect response the stimulus intensity is increased by the one size. A threshold is estimated from the mean midpoint of all runs. This estimate approaches, asymptotically, the correct threshold.

Bayesian and maximum-likelihood procedures

Bayesian and maximum-likelihood (ML) adaptive procedures behave, from the observer's perspective, similar to the staircase procedures. The choice of the next intensity level works differently, however: After each observer response, from the set of this and all previous stimulus/response pairs the likelihood is calculated of where the threshold lies. The point of maximum likelihood is then chosen as the best estimate for the threshold, and the next stimulus is presented at that level (since a decision at that level will add the most information). In a Bayesian procedure, a prior likelihood is further included in the calculation.^[24] Compared to staircase procedures, Bayesian and ML procedures are more time-consuming to implement but are considered to be more robust. Well-known procedures of this kind are Quest,^[26] ML-PEST,^[27] and Kontsevich & Tyler's method.^[28]

Magnitude estimation

In the prototypical case, people are asked to assign numbers in proportion to the magnitude of the stimulus. This psychometric function of the geometric means of their numbers is often a power law with stable, replicable exponent. Although contexts can change the law & exponent, that change too is stable and replicable. Instead of numbers, other sensory or cognitive dimensions can be used to match a stimulus and the method then becomes "magnitude production" or "cross-modality matching". The exponents of those dimensions found in numerical magnitude estimation predict the exponents found in magnitude production. Magnitude estimation generally finds lower exponents for the psychophysical function than multiple-category responses, because of the restricted range of the categorical anchors, such as those used by Likert as items in attitude scales.^[29]

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.^[38] 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.^[39]^[40] 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.^[41]

https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface

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.^[58]

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.^[59]

https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface

Pupil-size oscillation

A 2016 article^[101] 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.

https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface

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.^[102]^[103]

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).^[14]

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.^[104] (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.^[105] In 2009 it was reported noninvasive EEG control of a robotic arm using a CNV flip-flop.^[106] In 2011 it was reported control of two robotic arms solving Tower of Hanoi task with three disks using a CNV flip-flop.^[107] In 2015 it was described EEG-emulation of a Schmitt trigger, flip-flop, demultiplexer, and modem.^[108]

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.^[109] 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.^[110] 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.^[111]

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.^[112]

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.^[113]

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.^[114]

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.^[115]

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.^[116] 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.^[117]

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.^[118] 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: (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.^[119]

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.^[120]

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.^[121]

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).^[122] SSVEPs are electrical responses to flickering visual stimuli with repetition rates over 6 Hz^[122] that are best found in the parietal and occipital scalp regions of the visual cortex.^[123]^[124]^[125] It was reported that with this BCI setup, all study participants were able to initiate the phone call with minimal practice in natural environments.^[126]

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.^[122]^[127] The CCA algorithm has been applied in other experiments investigating BCIs with claimed high performance in accuracy as well as speed.^[128] 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.^[122]

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.^[127]

Limitations

In 2011, researchers stated that continued work should address ease of use, performance robustness, reducing hardware and software costs.^[122]

One of the difficulties with EEG readings is the large susceptibility to motion artifacts.^[129] 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,^[127] 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,^[130] 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.^[124] 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.^[131] 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.^[132]^[133] In 2009 Alex Blainey, an independent researcher based in the UK, successfully used the Emotiv EPOC to control a 5 axis robot arm.^[134] 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.^[3]^[4] The current focus of research is user-to-user communication through analysis of neural signals.^[135]

DIY and open source BCI

In 2001, The OpenEEG Project^[136] 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.^[137] 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

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.^[138] 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.^[139]

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.^[140]

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.^[141]

In 2011 researchers from UC Berkeley published^[142] 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.^[143]^[144]^[145]

BCI control strategies in neurogaming

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.^[146] 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^[147] dataset 2 for motor imagery is the Filter Bank Common Spatial Pattern, developed by Ang et al. from A*STAR, Singapore.^[148]

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^[55] 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.^[146]

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, the user must gaze at one of the flashing or iterating symbols in order to interact with the system. It is, therefore, likely that the symbols could become irritating and uncomfortable to use during longer play sessions, which can often last more than an hour which may not be an ideal gameplay.

Another type of VEP used with applications is the P300 potential. The P300 event-related potential is a positive peak in the EEG that occurs at roughly 300 ms after the appearance of a target stimulus (a stimulus for which the user is waiting or seeking) or oddball stimuli. The P300 amplitude decreases as the target stimuli and the ignored stimuli grow more similar.The P300 is thought to be related to a higher level attention process or an orienting response using P300 as a control scheme has the advantage of the participant only having to attend limited training sessions. The first application to use the P300 model was the P300 matrix. Within this system, a subject would choose a letter from a grid of 6 by 6 letters and numbers. The rows and columns of the grid flashed sequentially and every time the selected "choice letter" was illuminated the user's P300 was (potentially) elicited. However, the communication process, at approximately 17 characters per minute, was quite slow. The P300 is a BCI that offers a discrete selection rather than a continuous control mechanism. The advantage of P300 use within games is that the player does not have to teach himself/herself how to use a completely new control system and so only has to undertake short training instances, to learn the gameplay mechanics and basic use of the BCI paradigm.^[146]

Synthetic telepathy/silent communication

In a $6.3 million US Army initiative to invent devices for telepathic communication, Gerwin Schalk, underwritten in a $2.2 million grant, found the use of ECoG signals can discriminate the vowels and consonants embedded in spoken and imagined words, shedding light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.^[97]^[149]

In 2002 Kevin Warwick had an array of 100 electrodes fired into his nervous system in order to link his nervous system into the Internet to investigate enhancement possibilities. With this in place Warwick successfully carried out a series of experiments. With electrodes also implanted into his wife's nervous system, they conducted the first direct electronic communication experiment between the nervous systems of two humans.^[150]^[151]^[152]^[153]

Another group of researchers was able to achieve conscious brain-to-brain communication between two people separated by a distance using non-invasive technology that was in contact with the scalp of the participants. The words were encoded by binary streams using the sequences of 0's and 1's by the imaginary motor input of the person "emitting" the information. As the result of this experiment, pseudo-random bits of the information carried encoded words "hola" ("hi" in Spanish) and "ciao" ("goodbye" in Italian) and were transmitted mind-to-mind between humans separated by a distance, with blocked motor and sensory systems, which has low to no probability of this happening by chance.Conscious Brain-to-Brain Communication in Humans Using Non-Invasive Technologies

In the 1960s a researcher was successful after some training in using EEG to create Morse code using their brain alpha waves. Research funded by the US army is being conducted with the goal of allowing users to compose a message in their head, then transfer that message with just the power of thought to a particular individual.^[154] On 27 February 2013 the group with Miguel Nicolelis at Duke University and IINN-ELS successfully connected the brains of two rats with electronic interfaces that allowed them to directly share information, in the first-ever direct brain-to-brain interface.^[155]^[156]^[157]

Cell-culture BCIs

Researchers have built devices to interface with neural cells and entire neural networks in cultures outside animals. As well as furthering research on animal implantable devices, experiments on cultured neural tissue have focused on building problem-solving networks, constructing basic computers and manipulating robotic devices. Research into techniques for stimulating and recording from individual neurons grown on semiconductor chips is sometimes referred to as neuroelectronics or neurochips.^[158]

The world's first Neurochip, developed by Caltech researchers Jerome Pine and Michael Maher

Development of the first working neurochip was claimed by a Caltech team led by Jerome Pine and Michael Maher in 1997.^[159] The Caltech chip had room for 16 neurons.

In 2003 a team led by Theodore Berger, at the University of Southern California, started work on a neurochip designed to function as an artificial or prosthetic hippocampus. The neurochip was designed to function in rat brains and was intended as a prototype for the eventual development of higher-brain prosthesis. The hippocampus was chosen because it is thought to be the most ordered and structured part of the brain and is the most studied area. Its function is to encode experiences for storage as long-term memories elsewhere in the brain.^[160]

In 2004 Thomas DeMarse at the University of Florida used a culture of 25,000 neurons taken from a rat's brain to fly a F-22 fighter jet aircraft simulator.^[161] After collection, the cortical neurons were cultured in a petri dish and rapidly began to reconnect themselves to form a living neural network. The cells were arranged over a grid of 60 electrodes and used to control the pitch and yaw functions of the simulator. The study's focus was on understanding how the human brain performs and learns computational tasks at a cellular level.

Collaborative BCIs

The idea of combining/integrating brain signals from multiple individuals was introduced at Humanity+ @Caltech, in December 2010, by a Caltech researcher at JPL, Adrian Stoica; Stoica referred to the concept as multi-brain aggregation.^[162]^[163]^[164] A provisional patent application was filed on January 19, 2011, with the non-provisional patent following one year later.^[165] In May 2011, Yijun Wang and Tzyy-Ping Jung published, "A Collaborative Brain-Computer Interface for Improving Human Performance", and in January 2012 Miguel Eckstein published, "Neural decoding of collective wisdom with multi-brain computing".^[166]^[167] Stoica's first paper on the topic appeared in 2012, after the publication of his patent application.^[168] Given the timing of the publications between the patent and papers, Stoica, Wang & Jung, and Eckstein independently pioneered the concept, and are all considered as founders of the field. Later, Stoica would collaborate with University of Essex researchers, Riccardo Poli and Caterina Cinel.^[169]^[170] The work was continued by Poli and Cinel, and their students: Ana Matran-Fernandez, Davide Valeriani, and Saugat Bhattacharyya.^[171]^[172]^[173]

Ethical considerations

Sources:^[174]^[175]^[176]^[177]^[178]

User-centric issues

Long-term effects to the user remain largely unknown.
Obtaining informed consent from people who have difficulty communicating.
The consequences of BCI technology for the quality of life of patients and their families.
Health-related side-effects (e.g. neurofeedback of sensorimotor rhythm training is reported to affect sleep quality).
Therapeutic applications and their potential misuse.
Safety risks
Non-convertibility of some of the changes made to the brain

Legal and social

Issues of accountability and responsibility: claims that the influence of BCIs overrides free will and control over sensory-motor actions, claims that cognitive intention was inaccurately translated due to a BCI malfunction.
Personality changes involved caused by deep-brain stimulation.
Concerns regarding the state of becoming a "cyborg" - having parts of the body that are living and parts that are mechanical.
Questions personality: what does it mean to be a human?
Blurring of the division between human and machine and inability to distinguish between human vs. machine-controlled actions.
Use of the technology in advanced interrogation techniques by governmental authorities.
Selective enhancement and social stratification.
Questions of research ethics regarding animal experimentation
Questions of research ethics that arise when progressing from animal experimentation to application in human subjects.
Moral questions
Mind reading and privacy.
Tracking and "tagging system"
Mind control.
Movement control
Emotion control

In their current form, most BCIs are far removed from the ethical issues considered above. They are actually similar to corrective therapies in function. Clausen stated in 2009 that "BCIs pose ethical challenges, but these are conceptually similar to those that bioethicists have addressed for other realms of therapy".^[174] Moreover, he suggests that bioethics is well-prepared to deal with the issues that arise with BCI technologies. Haselager and colleagues^[175] pointed out that expectations of BCI efficacy and value play a great role in ethical analysis and the way BCI scientists should approach media. Furthermore, standard protocols can be implemented to ensure ethically sound informed-consent procedures with locked-in patients.

The case of BCIs today has parallels in medicine, as will its evolution. Similar to how pharmaceutical science began as a balance for impairments and is now used to increase focus and reduce need for sleep, BCIs will likely transform gradually from therapies to enhancements.^[177] Efforts are made inside the BCI community to create consensus on ethical guidelines for BCI research, development and dissemination.^[178] As innovation continues, ensuring equitable access to BCIs will be crucial, failing which generational inequalities can arise which can adversely affect the right to human flourishing.^[179]

The ethical considerations of BCIs are essential to the development of future implanted devices. End-users, ethicists, researchers, funding agencies, physicians, corporations, and all others involved in BCI use should consider the anticipated, and unanticipated, changes that BCIs will have on human autonomy, identity, privacy, and more.^[71]

Low-cost BCI-based interfaces

Recently a number of companies have scaled back medical grade EEG technology to create inexpensive BCIs for research as well as entertainment purposes. For example, toys such as the NeuroSky and Mattel MindFlex have seen some commercial success.

In 2006 Sony patented a neural interface system allowing radio waves to affect signals in the neural cortex.^[180]
In 2007 NeuroSky released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.^[181]
In 2008 OCZ Technology developed a device for use in video games relying primarily on electromyography.^[182]
In 2008 Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create a game, Judecca.^[183]^[184]
In 2009 Mattel partnered with NeuroSky to release the Mindflex, a game that used an EEG to steer a ball through an obstacle course. It is by far the best selling consumer based EEG to date.^[183]^[185]
In 2009 Uncle Milton Industries partnered with NeuroSky to release the Star Wars Force Trainer, a game designed to create the illusion of possessing the Force.^[183]^[186]
In 2009 Emotiv released the EPOC, a 14 channel EEG device that can read 4 mental states, 13 conscious states, facial expressions, and head movements. The EPOC is the first commercial BCI to use dry sensor technology, which can be dampened with a saline solution for a better connection.^[187]
In November 2011 Time magazine selected "necomimi" produced by Neurowear as one of the best inventions of the year. The company announced that it expected to launch a consumer version of the garment, consisting of catlike ears controlled by a brain-wave reader produced by NeuroSky, in spring 2012.^[188]
In February 2014 They Shall Walk (a nonprofit organization fixed on constructing exoskeletons, dubbed LIFESUITs, for paraplegics and quadriplegics) began a partnership with James W. Shakarji on the development of a wireless BCI.^[189]
In 2016, a group of hobbyists developed an open-source BCI board that sends neural signals to the audio jack of a smartphone, dropping the cost of entry-level BCI to £20.^[190] Basic diagnostic software is available for Android devices, as well as a text entry app for Unity.^[191]
In 2020, NextMind released a dev kit including an EEG headset with dry electrodes at $399.^[192]^[193] The device can be played with some demo applications or developers can create their own use cases using the provided Software Development Kit.

Future directions

Brain-computer interface

A consortium consisting of 12 European partners has completed a roadmap to support the European Commission in their funding decisions for the new framework program Horizon 2020. The project, which was funded by the European Commission, started in November 2013 and published a roadmap in April 2015.^[194] A 2015 publication led by Dr. Clemens Brunner describes some of the analyses and achievements of this project, as well as the emerging Brain-Computer Interface Society.^[195] For example, this article reviewed work within this project that further defined BCIs and applications, explored recent trends, discussed ethical issues, and evaluated different directions for new BCIs.

Other recent publications too have explored future BCI directions for new groups of disabled users (e.g.,^[11]^[196])

Disorders of consciousness (DOC)

Some persons have a disorder of consciousness (DOC). This state is defined to include persons with coma, as well as persons in a vegetative state (VS) or minimally conscious state (MCS). New BCI research seeks to help persons with DOC in different ways. A key initial goal is to identify patients who are able to perform basic cognitive tasks, which would of course lead to a change in their diagnosis. That is, some persons who are diagnosed with DOC may in fact be able to process information and make important life decisions (such as whether to seek therapy, where to live, and their views on end-of-life decisions regarding them). Some persons who are diagnosed with DOC die as a result of end-of-life decisions, which may be made by family members who sincerely feel this is in the patient's best interests. Given the new prospect of allowing these patients to provide their views on this decision, there would seem to be a strong ethical pressure to develop this research direction to guarantee that DOC patients are given an opportunity to decide whether they want to live.^[197]^[198]

These and other articles describe new challenges and solutions to use BCI technology to help persons with DOC. One major challenge is that these patients cannot use BCIs based on vision. Hence, new tools rely on auditory and/or vibrotactile stimuli. Patients may wear headphones and/or vibrotactile stimulators placed on the wrists, neck, leg, and/or other locations. Another challenge is that patients may fade in and out of consciousness, and can only communicate at certain times. This may indeed be a cause of mistaken diagnosis. Some patients may only be able to respond to physicians' requests during a few hours per day (which might not be predictable ahead of time) and thus may have been unresponsive during diagnosis. Therefore, new methods rely on tools that are easy to use in field settings, even without expert help, so family members and other persons without any medical or technical background can still use them. This reduces the cost, time, need for expertise, and other burdens with DOC assessment. Automated tools can ask simple questions that patients can easily answer, such as "Is your father named George?" or "Were you born in the USA?" Automated instructions inform patients that they may convey yes or no by (for example) focusing their attention on stimuli on the right vs. left wrist. This focused attention produces reliable changes in EEG patterns that can help determine that the patient is able to communicate. The results could be presented to physicians and therapists, which could lead to a revised diagnosis and therapy. In addition, these patients could then be provided with BCI-based communication tools that could help them convey basic needs, adjust bed position and HVAC (heating, ventilation, and air conditioning), and otherwise empower them to make major life decisions and communicate.^[199]^[200]^[201]

Motor recovery

People may lose some of their ability to move due to many causes, such as stroke or injury. Research in recent years has demonstrated the utility of EEG-based BCI systems in aiding motor recovery and neurorehabilitation in patients who have had a stroke.^[202]^[203]^[204]^[205] Several groups have explored systems and methods for motor recovery that include BCIs.^[206]^[207]^[208]^[209] In this approach, a BCI measures motor activity while the patient imagines or attempts movements as directed by a therapist. The BCI may provide two benefits: (1) if the BCI indicates that a patient is not imagining a movement correctly (non-compliance), then the BCI could inform the patient and therapist; and (2) rewarding feedback such as functional stimulation or the movement of a virtual avatar also depends on the patient's correct movement imagery.

So far, BCIs for motor recovery have relied on the EEG to measure the patient's motor imagery. However, studies have also used fMRI to study different changes in the brain as persons undergo BCI-based stroke rehab training.^[210]^[211]^[212] Imaging studies combined with EEG-based BCI systems hold promise for investigating neuroplasticity during motor recovery post-stroke.^[212] Future systems might include the fMRI and other measures for real-time control, such as functional near-infrared, probably in tandem with EEGs. Non-invasive brain stimulation has also been explored in combination with BCIs for motor recovery.^[213] In 2016, scientists out of the University of Melbourne published preclinical proof-of-concept data related to a potential brain-computer interface technology platform being developed for patients with paralysis to facilitate control of external devices such as robotic limbs, computers and exoskeletons by translating brain activity.^[214]^[215] Clinical trials are currently underway.^[216]

Functional brain mapping

Each year, about 400,000 people undergo brain mapping during neurosurgery. This procedure is often required for people with tumors or epilepsy that do not respond to medication.^[217] During this procedure, electrodes are placed on the brain to precisely identify the locations of structures and functional areas. Patients may be awake during neurosurgery and asked to perform certain tasks, such as moving fingers or repeating words. This is necessary so that surgeons can remove only the desired tissue while sparing other regions, such as critical movement or language regions. Removing too much brain tissue can cause permanent damage, while removing too little tissue can leave the underlying condition untreated and require additional neurosurgery. Thus, there is a strong need to improve both methods and systems to map the brain as effectively as possible.

In several recent publications, BCI research experts and medical doctors have collaborated to explore new ways to use BCI technology to improve neurosurgical mapping. This work focuses largely on high gamma activity, which is difficult to detect with non-invasive means. Results have led to improved methods for identifying key areas for movement, language, and other functions. A recent article addressed advances in functional brain mapping and summarizes a workshop.^[218]

Flexible devices

Flexible electronics are polymers or other flexible materials (e.g. silk,^[219] pentacene, PDMS, Parylene, polyimide^[220]) that are printed with circuitry; the flexible nature of the organic background materials allowing the electronics created to bend, and the fabrication techniques used to create these devices resembles those used to create integrated circuits and microelectromechanical systems (MEMS).^{[citation needed]} Flexible electronics were first developed in the 1960s and 1970s, but research interest increased in the mid-2000s.^[221]

Flexible neural interfaces have been extensively tested in recent years in an effort to minimize brain tissue trauma related to mechanical mismatch between electrode and tissue.^[222] Minimizing tissue trauma could, in theory, extend the lifespan of BCIs relying on flexible electrode-tissue interfaces.

Neural dust

Neural dust is a term used to refer to millimeter-sized devices operated as wirelessly powered nerve sensors that were proposed in a 2011 paper from the University of California, Berkeley Wireless Research Center, which described both the challenges and outstanding benefits of creating a long lasting wireless BCI.^[223]^[224] In one proposed model of the neural dust sensor, the transistor model allowed for a method of separating between local field potentials and action potential "spikes", which would allow for a greatly diversified wealth of data acquirable from the recordings.^[223]

See also
Informatics
Intendix (2009)
AlterEgo, a system that reads unspoken verbalizations and responds with bone-conduction headphones
Augmented learning
Biological machine
Cortical implants
Deep brain stimulation
Human senses
Kernel (neurotechnology company)
Lie detection
Microwave auditory effect
Neural engineering
Neuralink
Neurorobotics
Neurostimulation
Nootropic
Project Cyborg
Simulated reality
Telepresence
Thought identification
Wetware computer (Uses similar technology for IO)
Whole brain emulation

https://en.wikipedia.org/wiki/Microwave_auditory_effect

https://en.wikipedia.org/wiki/Telepresence

https://en.wikipedia.org/wiki/Brain-reading

https://en.wikipedia.org/wiki/Mind_uploading

https://en.wikipedia.org/wiki/Molecular_machine#Biological

https://en.wikipedia.org/wiki/Functional_near-infrared_spectroscopy

https://en.wikipedia.org/wiki/Patch_clamp

https://en.wikipedia.org/wiki/Mainframe_computer

https://en.wikipedia.org/wiki/Phosphene

https://en.wikipedia.org/wiki/Transfection

https://en.wikipedia.org/wiki/Light-gated_ion_channel

https://en.wikipedia.org/wiki/Channelrhodopsin

https://en.wikipedia.org/wiki/Scar

https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface#Synthetic_telepathy/silent_communication

https://en.wikipedia.org/wiki/Sensory_substitution

https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface

https://en.wikipedia.org/wiki/Connectomics

https://en.wikipedia.org/wiki/Systems_neuroscience

https://en.wikipedia.org/wiki/Neuropathology

https://en.wikipedia.org/wiki/Neuro-ophthalmology

https://en.wikipedia.org/wiki/Neuroradiology

https://en.wikipedia.org/wiki/Neurosurgery

https://en.wikipedia.org/wiki/Neurovirology

https://en.wikipedia.org/wiki/Chronobiology

https://en.wikipedia.org/wiki/Molecular_cellular_cognition

https://en.wikipedia.org/wiki/Global_neurosurgery

https://en.wikipedia.org/wiki/Neuroanthropology

https://en.wikipedia.org/wiki/Neural_engineering

https://en.wikipedia.org/wiki/Neurotechnology

https://en.wikipedia.org/wiki/Neurocriminology

https://en.wikipedia.org/wiki/Neuroesthetics

https://en.wikipedia.org/wiki/Neuroethics

https://en.wikipedia.org/wiki/Neuroethology

https://en.wikipedia.org/wiki/Neuromorphic_engineering

https://en.wikipedia.org/wiki/Neurophenomenology

https://en.wikipedia.org/wiki/Artificial_neural_network

https://en.wikipedia.org/wiki/Neural_circuit

https://en.wikipedia.org/wiki/Neurochip

https://en.wikipedia.org/wiki/Neurodegenerative_disease

https://en.wikipedia.org/wiki/Neurodevelopmental_disorder

https://en.wikipedia.org/wiki/Neurogenesis

https://en.wikipedia.org/wiki/Neuroimaging

https://en.wikipedia.org/wiki/Neuromodulation

https://en.wikipedia.org/wiki/Neurotoxin

https://en.wikipedia.org/wiki/Arsenic

https://en.wikipedia.org/wiki/marburg

https://en.wikipedia.org/wiki/de-boning/de-blooding

https://en.wikipedia.org/wiki/fluid-generator-removal

https://en.wikipedia.org/wiki/Insecticide

https://en.wikipedia.org/wiki/Ammunition

https://en.wikipedia.org/wiki/Envelope_(mathematics)

draft

Blog Archive

Sunday, February 12, 2023

02-12-2023-0821 - CONSCIOUSNESS, CONSCIOUSNESS AFTER DEATH, NODE HOP, NETWORK WIRELESS, COMPUTERS, ETC. draft

History

Detection

Discrimination

Classical psychophysical methods

Method of limits

Method of constant stimuli

Method of adjustment

Adaptive psychophysical methods

Staircase procedures

Bayesian and maximum-likelihood procedures

Magnitude estimation

See also

Nicolelis

Vision

Pupil-size oscillation

Functional near-infrared spectroscopy

Electroencephalography (EEG)-based brain-computer interfaces

Dry active electrode arrays

SSVEP mobile EEG BCIs

Limitations

Prosthesis and environment control

DIY and open source BCI

MEG and MRI

BCI control strategies in neurogaming

Motor imagery

Bio/neurofeedback for passive BCI designs

Visual evoked potential (VEP)

Synthetic telepathy/silent communication

Cell-culture BCIs

Collaborative BCIs

Ethical considerations

User-centric issues

Legal and social

Low-cost BCI-based interfaces

Future directions

Disorders of consciousness (DOC)

Motor recovery

Functional brain mapping

Flexible devices

Neural dust

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