Automatic EOG Artifact Removal in Brain-computer Interface Systems
Wei-Yen Hsu, Cheng-Xuan Li, Meng-Chen Li, Hui-Yu Tien
Journal of Multimedia Processing and Technologies, Vol. 9, No. 4, 2018.
DOI: https://doi.org/10.6025/jmpt/2018/9/4/117-123
Link: http://www.dline.info/jmpt/fulltext/v9n4/jmptv9n4_1.pdf
Abstract:
In this study, we propose a system to recognize the finger-lifting electroencephalogram (EEG) data. Combined with independent component analysis (ICA) and feature extraction, fuzzy c-means (FCM) clustering is used to discriminate
between left and right finger movement without supervision. ICA is used to eliminate the electrooculography (EOG) artifacts.
Wavelet-fractal features are then extracted from wavelet data via fractal dimension. FCM clustering is used for feature
discrimination. It is an unsupervised approach suitable for the applications of biomedical signals. After EOG artifact removal, the performance is improved for all subjects.