A novel supervised locality sensitive Factor analysis to classify voluntary hand movement in multi direction using EEG source space

Abstract

Recent advances in EEG-based brain-computer interfaces (BCIs) have shown that brain signals can be used to decode arm movement intention and execution in multiple directions. Conventional approaches use sensor space EEG for classifying movement related tasks. Sensor-space EEG can reveal only limited information about the trivial but complex tasks that involve higher degrees of freedom of the movement. On the contrary, source space analysis is expected to provide more information about the neurophysiological mechanism relevant to the task. To this end, we propose a novel source-space feature extraction technique based on supervised locality sensitive Factor analysis which approximates the neurophysiological functioning of our experimental data in a better way than that of a solely data-driven approach. EEG recordings in the sensor space are transformed into source space using the Weighted Minimum Norm Estimate (wMNE) method. We show that for a multi-class classification problem of classifying the EEG of voluntary arm movement in 4 orthogonal directions, the source space features offer a significant improvement in the classification accuracy compared to sensor space features. One-versus-rest (OVR) approach is used for multiclass classification with Fisher's Linear Discriminant (FLD) as the primary classifier.