Skewness based discreate wavelet Transform and Swarm decomposition for the automatic elimination of ocular artifacts from EEG signals

EEG signal reliability in biomedical applications may be affected by ocular artifacts resulting from blinks and eye movements. Existing methods often struggle to remove these artifacts effectively. In order to overcome this constraint, we suggest a new framework that integrating Skewness-based Discrete Wavelet Transform (SDWT) with the Swarm Decomposition (SwD) algorithm. The SDWT separates EEG signals into artifact and non-artifact segments. The artifact segment undergoes SwD processing, which decomposes it into hidden components. The EOG (electrooculographic) component is then analyzed using energy and skewness metrics. The level corresponding to the artifact identified by its highest energy and skewness is discarded, and the remaining elements are recombined with the non-artifact segment. To automate EOG identification, the K-means clustering algorithm is employed. We evaluate the filtering performance of the proposed method using metric: Mean absolute error in the power spectrum (MAE-PS), in power spectrum analysis across the delta(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>δ</mml:mi></mml:mrow></mml:math>), theta (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>θ</mml:mi></mml:mrow></mml:math>), alpha (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>), and beta (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math>) frequency bands. Testing was conducted on two semi-simulated and one real-time datasets: Publicly available EEG signals contaminated by eye-movement and eye-blink artifact. Wearable EEG recordings from epileptic and healthy persons performing physical activities (climbing stairs, sitting, running, and walking). Results demonstrate that our method outperforms existing techniques, achieving: Lower MAE-PS in power spectrum analysis across <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi></mml:mrow><mml:mtext>,</mml:mtext></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mtext>,</mml:mtext></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>θ</mml:mi></mml:mrow></mml:math> bands, indicating better signal preservation. This approach shows promise for enhancing EEG reliability in both clinical and mobile settings.