Editing Brain–computer interface (section)

====BCI control strategies in neurogaming====

=====Motor imagery=====

[[Motor imagery]] involves imagining the movement of body parts, activating the [[sensorimotor cortex]], which modulates sensorimotor oscillations in the EEG. This can be detected by the BCI and used to infer user intent. Motor imagery typically requires training to acquire acceptable control. Training sessions typically consume hours over several days. 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.<ref name="ieeexplore.ieee.org">{{cite journal| vauthors = Marshall D, Coyle D, Wilson S, Callaghan M |title=Games, Gameplay, and BCI: The State of the Art|journal=IEEE Transactions on Computational Intelligence and AI in Games|volume=5|issue=2|page = 83|doi=10.1109/TCIAIG.2013.2263555 |year=2013|s2cid=206636315}}</ref> Machine learning methods were used to compute a subject-specific model for detecting motor imagery performance. The top performing algorithm from BCI Competition IV in 2022<ref>{{cite web|url=http://www.bbci.de/competition/iv/|title=Goals of the organizers|publisher=BBC|access-date=19 December 2022}}</ref> dataset 2 for motor imagery was the Filter Bank Common Spatial Pattern, developed by Ang et al. from [[A*STAR]], [[Singapore]].<ref>{{cite journal | vauthors = Ang KK, Chin ZY, Wang C, Guan C, Zhang H | title = Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b | journal = Frontiers in Neuroscience | volume = 6 | page = 39 | date = 1 January 2012 | pmid = 22479236 | pmc = 3314883 | doi = 10.3389/fnins.2012.00039 | doi-access = free }}</ref>

=====Bio/neurofeedback for passive BCI designs=====
Biofeedback can be used to monitor a subject's mental relaxation. In some cases, biofeedback does not match EEG, while parameters such as [[electromyography]] (EMG), [[galvanic skin response|galvanic skin resistance]] (GSR), and [[heart rate variability]] (HRV) can do so. Many biofeedback systems treat disorders such as [[Attention deficit hyperactivity disorder|attention deficit hyperactivity disorder (ADHD)]], sleep problems in children, teeth grinding, and chronic pain. EEG biofeedback systems typically monitor four brainwave bands (theta: 4–7&nbsp;Hz, alpha:8–12&nbsp;Hz, SMR: 12–15&nbsp;Hz, beta: 15–18&nbsp;Hz) and challenge the subject to control them. Passive BCI uses BCI to enrich human–machine interaction with information on the user's mental state, for example, simulations that detect when users intend to push brakes during emergency vehicle braking.<ref name=":0" /> Game developers using passive BCIs understand that through repetition of game levels the user's cognitive state adapts. During the first play of a given level, the player reacts differently than during subsequent plays: for example, the user is less surprised by an event that they expect.<ref name="ieeexplore.ieee.org"/>

=====Visual evoked potential (VEP)=====

A VEP is an electrical potential recorded after a subject is presented with a visual stimuli. The types of VEPs include SSVEPs and P300 potential.

[[Steady state visually evoked potential|Steady-state visually evoked potential]]s (SSVEPs) use potentials generated by exciting the [[retina]], using visual stimuli modulated at certain frequencies. SSVEP stimuli are often formed from alternating checkerboard patterns and at times use flashing images. The frequency of the phase reversal of the stimulus used can be distinguished by EEG; this makes detection of SSVEP stimuli relatively easy. SSVEP is used 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. Electrocardiographic artefacts do not affect the frequencies monitored. The SSVEP signal is robust; the topographic organization of the primary visual cortex is such that a broader area obtains afferents from the visual field's central or fovial region. SSVEP comes with problems. As SSVEPs use flashing stimuli to infer user 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 become irritating and uncomfortable during longer play sessions.

Another type of VEP is the [[P300 (neuroscience)|P300 potential]]. This potential is a positive peak in the EEG that occurs roughly 300 ms after the appearance of a target stimulus (a stimulus for which the user is waiting or seeking) or [[Oddball paradigm|oddball stimuli]]. P300 amplitude decreases as the target stimuli and the ignored stimuli grow more similar. P300 is thought to be related to a higher level attention process or an orienting response. Using P300 requires fewer training sessions. The first application to use it was the P300 matrix. Within this system, a subject chooses a letter from a 6 by 6 grid of 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 slow. P300 offers a discrete selection rather than continuous control. The advantage of P300 within games is that the player does not have to learn how to use a new control system, requiring only short training instances to learn gameplay mechanics and the basic BCI paradigm.<ref name="ieeexplore.ieee.org"/>