EEG Electrode Selection for a Two-Class Motor Imagery Task in a BCI Using fNIRS Prior Data

This study investigated the possibility of using functional near infrared spectroscopy (fNIRS) during right- and left-hand motor imagery tasks to select an optimum set of electroencephalography (EEG) electrodes for a brain computer interface. fNIRS has better spatial resolution allowing areas of brain activity to more readily be identified. The ReliefF algorithm was used to identify the most reliable fNIRS channels. Then, EEG electrodes adjacent to those channels were selected for classification. This study used three different classifiers of linear and quadratic discriminant analyses, and support vector machine to examine the proposed method.Clinical Relevance— Reducing the number of sensors in a BCI makes the system more usable for patients with severe disabilities.

I. Introduction

Electroencephalography (EEG) is the most frequently used imaging technique in brain computer interfaces (BCIs). It is a non-invasive technique that offers high temporal resolution, low cost, and portability. A drawback of EEG is its relatively poor spatial resolution compared to functional magnetic resonance imaging (fMRI) and functional near infrared spectroscopy (fNIRS) [1]. The brain’s electrical activities are produced by voltage change across the neurons’ cell membranes. The electrical activity generated by a single neuron is too small to be detected by EEG electrodes. Therefore, each EEG electrode records electrical activities due to many neurons in the brain. Each EEG signal consists of a combination of signals originating from different areas of the brain [2]. When attempting to detect EEG signals, the skull and the scalp attenuate the electrical signals produced by the brain’s neuronal activities making it more difficult for an EEG system to identify the electrical current source in the brain [3].