Toward understanding intra-user variability in BCI : a multi-session, multi-context experimental protocol
Résumé
Brain–computer interface (BCI) performance and EEG signals fluctuate as a result of interacting sources of variability, including experimental context, time of day, and user-related states such as engagement and fatigue. Although machine learning approaches provide effective tools to manage with these fluctuations, the specific factors influencing BCI performance and EEG features remain insufficiently characterized. To address the lack of empirical studies on intra-user variability in brain–computer interfaces (BCIs), a six-month experimental campaign involving 22 participants was conducted. The protocol was designed to capture performance and EEG fluctuations across time, interaction contexts, and time of day. Multiple sessions were completed over several weeks, during which different mental tasks (motor imagery, mental calculation, and letter/word association) were performed under two BCI interfaces: a low-stimulating Graz paradigm and a more stimulating BrainHero interface with background noise. EEG data, behavioral performance, and repeated questionnaires assessing psychological states (e.g., sleepiness, fatigue) and user experience (e.g., interest, engagement) were collected. This protocol provides a comprehensive framework to study the dynamics of intra-user variability in active BCIs.
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