Wavlet phase-locking based binary classification of hand movement directions from EEG.

OBJECTIVE: Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions.

APPROACH: In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions. EEG data collected from seven subjects performing right hand movements in four orthogonal directions in the horizontal plane is used for this analysis.

MAIN RESULTS: Our proposed W-PLV based method achieves a mean binary classification accuracy of 76.85% over seven subjects using wavelet levels corresponding to ⩽12 Hz EEG. The results also show direction-dependent information in various wavelet levels and indicate the presence of relevant information in slow cortical potentials (<1 Hz) as well as higher wavelet levels (⩽12 Hz).

SIGNIFICANCE: This study presents a thorough analysis of the phase-locking patterns extracted from EEG corresponding to hand movements in different directions using W-PLV across various wavelet levels and verifies their discriminative ability in the single trial binary classification of hand movement directions.