Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson’s correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.20, median r = 0.36, upper quartile r = 0.52) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis in alpha (8-13Hz) and beta (20-30 Hz) EEG respectively also showed focused bilateral alpha event related desynchronizations (ERDs) over central scalp areas and contralateral beta event related synchronizations (ERSs) localized to central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.