Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals

The movement of eyeballs and eye blinks produces ocular artifacts in the electroencephalogram (EEG) signal during recording. It is necessary to filter out these artifacts from the EEG signal for different biomedical applications. The state-of-the-art (SOTA) methods have less denoising performance to filter ocular artifacts from EEG signals. To overcome this, we have proposed a novel filter-bank-based hybrid approach in this paper to eliminate ocular artifacts from EEG signals. The optimal filter-bank based on the dyadic boundary points based empirical wavelet transform (DBPEWT) is introduced for the multiscale decomposition of EEG signal into various sub-band (SB) signals. The Savitzky–Golay-driven total variation (SGDTV) filter is formulated and applied to the contaminated EEG’s low-frequency range sub-band or first sub-band signal to obtain the filtered sub-band signal. The filtered EEG signal is calculated based on the linear combination of the filtered first sub-band signal evaluated from the SGDTV stage with other sub-band signals of the contaminated EEG signal. The filtering performance of the proposed approach is assessed using the mean absolute error (MAE) in the power spectrum (PS) for θ, α, and β-bands of contaminated and filtered EEG signals. The suggested approach is evaluated using the eye-blink, eye-movement-based ocular artifact-contaminated EEG signals from two public databases. Furthermore, the denoising performance of the proposed filter-bank approach is evaluated using the wearable EEG sensor signals from healthy and epilepsy subjects while performing physical activities such as sitting, walking, stairs, and running, respectively. The results reveal that the proposed approach has obtained the average MAE-PS values for (θ, α, and β) bands as (0.156, 0.308, and 0.201), and (0.081, 0.089, and 0.088), respectively for the removal of eye-blink and eye-movement artifacts from EEG signals. The suggested approach has obtained the average MAE-PS values for (θ, α, and β-bands) as (4.555±0.203, 0.057±0.026, 0.062±0.005), and (4.085±0.086, 0.068±0.043, 0.048±0.006) for healthy and epilepsy subjects during running activity to eliminate artifacts from the wearable sensor-based EEG signals. The denoising performance of the proposed approach has been compared with existing methods to remove eye-movement and eye-blink artifacts from EEG signals.