Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers.

Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.