Designing a Motion-Onset Visual-Evoked Potential-Based Brain-Computer Interface to Control a Computer Game

• Published in: IEEE transactions on games Vol. 16; no. 2; pp. 409 - 418
• Main Authors: Amini, Mohammad Matin, Shalchyan, Vahid
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain–computer interface (BCI); Calibration; Classification; Computer & video games; computer game; Contraction; Discriminant analysis; Ear; Electroencephalography; electroencephalography (EEG); Feature extraction; Games; Human-computer interface; Information transfer; motion-onset visually evoked potentials (mVEP); Movement; Rotation; Stimulation; Stimuli; Target detection; Video games; Visualization
• ISSN: 2475-1502; 2475-1510
• DOI: 10.1109/TG.2023.3279289

Brain-computer interface (BCI) establishes an alternative direct communication channel between the brain and computer or other external devices. Motion-onset visual-evoked potential (mVEP) related to the individual perception of movement is among synchronous BCI system that utilizes external stimulus. mVEPs stimulation paradigm is like an oddball stimulation paradigm. Researchers have recently been investigating mVEPs to control a computer game. To control a computer game, they used translation stimulus to evoke these potentials in electroencephalography brain signals. In this study, we examine designing a computer game with four different types of stimuli, including translation, contraction, expansion, and rotation. We also used the time-domain and wavelet coefficients for the feature extraction. Also, we used different types of linear discriminant analysis (LDA) classification methods, including least absolute shrinkage and selection operator, stepwise LDA (SWLDA); moreover, and spatial-temporal discriminant analysis (STDA) for detecting the target stimulus. Our results demonstrate that contraction, expansion, and rotation stimuli, as well as translation, could be employed to control a BCI game. Our results showed that subjects got higher scores, game accuracy control, and information transfer rate using the rotation stimulus with SWLDA/STDA classification methods.