Automatic removal of both EOG and EMG artifacts in EEG by a fast ICA and an IVA

Xiaolong Li; Moxiong Tang; Ming Yu

doi:10.1117/12.2639044

23 May 2022 Automatic removal of both EOG and EMG artifacts in EEG by a fast ICA and an IVA

Xiaolong Li, Moxiong Tang, Ming Yu

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122541E (2022) https://doi.org/10.1117/12.2639044
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Electroencephalogram (EEG) signals are easy to be interfered by the artifacts of both electrooculogram (EOG) and electromyogram (EMG) during data acquisition. The two artifacts are unwanted in EEG signals processing but difficult to remove. In this paper, a novel blind source separation method for removal of both EOG and EMG based on the fast independent component analysis (FICA) and the independent vector analysis (IVA) is presented. The system uses both high-order statistics and second-order statistics to analyze the non-Gaussian and weak correlation of the artifact signals. Firstly, the FICA algorithm is used to decompose EEG into independent components. Next, the EOG artifacts will be identified and removed according to the window width of the separated EEG signals. Finally, an IVA Gauss-Laplace distribution (IVA-GLD) algorithm with an adaptive step size is exploited to remove the EMG artifacts. Experiments were carried out by the data set collected by a self-developed equipment. The root mean square error (RMSE) of the proposed method is 19.431, which is lower than the similar method without the peak window (23.118), and the correlation coefficient (CC) is 0.895, which is higher than the similar method without the peak window (0.799).

Citation Download Citation

Xiaolong Li, Moxiong Tang, and Ming Yu "Automatic removal of both EOG and EMG artifacts in EEG by a fast ICA and an IVA", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122541E (23 May 2022); https://doi.org/10.1117/12.2639044

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