Sotirios Papadopoulos

This paper introduces a new method, lagged Hilbert autocoherence (LHaC), to better quantify neural rhythmicity compared to existing Fourier-based methods.

The paper investigates beta burst activity in motor imagery tasks, showing that burst-based features can match or exceed traditional beta power methods in decoding brain signals.

This paper improves motor imagery-based BCI by analyzing beta burst waveforms instead of beta power, showing better classification performance and information transfer rates.

The paper identifies diverse beta burst waveform motifs in human MEG data during movement tasks and shows they reflect distinct computational processes.