Improvement of the Accuracy of SSVEP-BCI with In-Ear EEG Using Multiple Regression Analysis

Abstract

A brain-computer interface (BCI) is an interface that reads brain activity and operates a computer. Typically, BCI involves wearing a device on the head to measure brain waves. EEG meters are cumbersome for the user when worn and require individuality techniques to reduce impedance. In-Ear EEG, which is obtained from electrodes placed near the inner and outer ear, is expected to solve these problems. Users can use it to have a comfortable BCI experience. However, in-Ear EEG has the problem that the EEG used for BCI uses EEG located far from the actively observable areas of the brain, which results in poor signal quality. Steady state visual evoked potential (SSVEP) is an EEG that is predominantly observed in the primary visual cortex in the occipital region during gazing at a flashing stimulus, and SSVEP-BCI is expected to be used as a new communication tool. Ear EEG will greatly advance the social implementation of SSVEP-BCI. In this study, electrodes were placed at 10 locations near the ear and 4 locations on the back of the head. SSVEP-BCI with 28 different inputs including alphabets was designed and evaluated. The accuracy of the SSVEP-BCI with signals obtained from the occipital region was 84.92%, and that of the BCI with signals obtained from the ear was 28.17%. Using the occipital and intra-ear EEG data sets as the teacher data, multiple regression analysis was performed to improve the accuracy, and an accuracy of 55.83% was achieved. These results indicate that improvements are needed to make in-Ear EEG based SSVEP-BCI practical. It also suggests that a communication tool using in-Ear EEG based SSVEP-BCI may be feasible in the future.

Keywords: brain, computer interface, steady state visual evoked potential, canonical correlation analysis, ear electroencephalography, multiple regression analysis

DOI: 10.54941/ahfe1003953