Subband optimization for EEG-based classification of movements of the same limb

Abstract

The contribution investigates the impact of frequency feature optimization on discriminating between movement-related EEG realisations associated with right shoulder elevation and right index finger flexion movements. Exhaustive search of subbands in the range from 5 to 45 Hz is performed. A classifier based on Hidden Markov Models is utilised. The results show a large variability of optimal settings among subjects and electrodes. Using subband optimization an average 3.5% increase in classification accuracy of EEG filtered using 8-neighbor Laplacian filter was achieved, reaching an overall score of 81.2±1.2%, individual improvements ranging from 1.2 to 9.9%. The best general setting common for all subject was confirmed as 5-40 Hz.