Classification of mental arithmetic and resting-state based on Ear-EEG

Abstract

Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 %, central: 76.7 %, occipital: 82.6 % and ear: 75.6 %). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.