Abstract: To enhance the effectiveness of removing ocular artifacts from electroencephalogram (EEG) signals, an automatic ocular artifact removal algorithm is proposed that combines fast indepen- dent component analysis (FastICA) and heuristic wavelet thresholding (HWT), using fuzzy entropy as the criterion for identifying ocular artifacts. Firstly, a channel selection algorithm is employed to reduce the dimensionality of the original EEG signals, thereby improving computational efficiency. Subsequently, the FastICA algorithm is utilized to decompose the selected EEG signals into independent components. Then, fuzzy entropy analysis is conducted to identify the independent components containing ocular artifacts. Next, the HWT algorithm is applied to eliminate the ocular artifact components from those identified components while preserving the useful EEG signals. Finally, inverse wavelet transform and inverse ICA reconstruction are performed to obtain the artifact-free EEG signals. The proposed algorithm was validated using the BCI Competition IV dataset. The results indicate that, compared to existing algorithms, this algorithm performs well across multiple performance metrics, with a signal-to-noise ratio (SNR) improvement of approximately 12% compared to existing kurtosis-based artifact identification algorithms.

Key words: electroencephalogram(EEG) signal, channel selection, fast independent component analysis(FastICA), ocular artifact, artifact removal