Editing Brain–computer interface (section)

=====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>