A wearable brain-computer interface to play an endless runner game by self-paced motor imagery

• Published in: Journal of neural engineering Vol. 22; no. 2; pp. 26032 - 26047
• Main Authors: Arpaia, Pasquale, Esposito, Antonio, Galasso, Enza, Galdieri, Fortuna, Natalizio, Angela
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.04.2025
• Subjects: Adult; Brain-Computer Interfaces; Electroencephalography - instrumentation; Electroencephalography - methods; Female; gaming application; Humans; Imagination - physiology; low-density EEG; Male; motor imagery; Psychomotor Performance - physiology; real-time processing; Running - physiology; Running - psychology; self-paced BCI; Video Games; wearability; Wearable Electronic Devices; Young Adult; gaming application; motor imagery; low-density EEG; self-paced BCI; real-time processing; wearability
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/adc205

Objective. A wearable brain–computer interface is proposed and validated experimentally in relation to the real-time control of an endless runner game by self-paced motor imagery(MI). Approach. Electroencephalographic signals were recorded via eight wet electrodes. The processing pipeline involved a filter-bank common spatial pattern approach and the combination of three binary classifiers exploiting linear discriminant analysis. This enabled the discrimination between imagining left-hand, right-hand, and no movement. Each mental task corresponded to an avatar horizontal motion within the game. Twenty-three healthy subjects participated to the experiments and their data are made publicly available. A custom metric was proposed to assess avatar control performance during the gaming phase. The game consisted of two levels, and after each, participants completed a questionnaire to self-assess their engagement and gaming experience. Main results. The mean classification accuracies resulted 73%, 73%, and 67% for left-rest, right-rest, and left-right discrimination, respectively. In the gaming phase, subjects with higher accuracies for left-rest and right-rest pair exhibited higher performance in terms of the custom metric. Correlation of the offline and real-time performance was investigated. The left-right MI did not correlate to the gaming phase performance due to the poor mean accuracy of the calibration. Finally, the engagement questionnaires revealed that level 1 and level 2 were not perceived as frustrating, despite the increasing difficulty. Significance. The work contributes to the development of wearable and self-paced interfaces for real-time control. These enhance user experience by guaranteeing a more natural interaction with respect to synchronous neural interfaces. Moving beyond benchmark datasets, the work paves the way to future applications on mobile devices for everyday use.