Investigating ICA for EEG Electrode Optimization for The Differentiation Between Right-Hand and Left-Hand Movements

A bionic hand that is controlled by an electroencephalograph (EEG)-based brain computer interface (BCI) can aid motor impaired individuals to perform daily tasks. High-density EEG (128 electrodes) are suggested for the spatial resolution required to control these activities. This makes the system expensive, time-consuming to set up and uncomfortable for the user. This research explores the development of a novel electrode reduction method that combines independent component analysis (ICA) and features related to event-related desynchronization and synchronization (ERD/ERS) modulations to produce an optimised and reduced EEG electrode set. This method was tested for the differentiation between right-hand and left-hand movements. The results suggest that the optimal channel configuration produced was a 16-electrode configuration. The 16-electrode configuration obtained a classification accuracy of 70.51 %, using a linear support vector machine, which is a 12.01% loss in classification accuracy when compared to using the full 128-electrode set. This suggests that ICA could be used as a primary technique to reduce the number of electrodes of an EEG-based BCI controlling a bionic hand. The research also suggests that motor control information could be captured from widely distributed electrodes. Copyright (c) 2021 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)