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Description
Artifacts pose a significant challenge in the analysis of EEG signals. Physiological artifacts stem from natural activities of the human body, such as swallowing saliva, clenching the jaw, facial grimacing, and eye blinking, among others. Visual evaluation often serves as the basis for artifact elimination. In this study, the authors investigated the impact of artifacts on the detection of steady-state visually evoked potentials (SSVEPs). The article explored various techniques for artifact elimination, including linear regression, adaptive filters, and independent component analysis (ICA). The effectiveness of the algorithms was evaluated using classification accuracy as a metric. The results indicate that the most promising outcomes were achieved with independent component analysis. However, this method requires expert knowledge and may not always be feasible. On average, a 30% increase in the classification accuracy of evoked potentials was observed in signals cleaned using ICA. The linear regression method and the recursive least squares (RLS) adaptive filtering showed either improvement or no deterioration. Among the examined EEG signal-cleaning methods, the normalized least-mean-square (NLMS) filter exhibited the poorest performance.
