New Metrics of Event-Related (De)Synchronization Temporal Variability Explain Motor Imagery-based BCI Performance

Motor Imagery-based (MI) Brain-Computer Interface (BCI) detect imagined limb movements from ElectroEncephaloGraphy (EEG) to translate them into commands for various applications. They do so by analyzing sensorimotor EEG rhythms, typically event-related (de)synchronization (ERD/S) over the motor cortex. Despite MI task intuitiveness and their many BCI applications, not all users achieve sufficient MI classification accuracy, notably due to large intraand inter-user variability in ERD/S. Understanding this variability is thus crucial for finding ways to enhance BCI classification performance, but BCI variability metrics are lacking. Therefore, this paper proposes two new ERD/S variability metrics and studies, on a large MI-BCI dataset (N = 85 users), how these and two existing metrics can explain BCI performance.Results show that temporal variability of ERD/S—both within and across trials—negatively correlates (r = −0.28 to −0.34) with BCI performance in the within-user scenario (with a user-specific classifier). In the cross-users scenario (with a generic cross-user classifier), test users variability metrics, including ERD/S temporal and amplitude variability, were negatively correlated with performance (r = −0.30 to −0.39). These findings demonstrate the value of metrics to quantify ERD/S variability. They may also guide future design strategies for BCI user training or machine learning.