Motion artifact is observed in electroencephalogram (EEG) signals during the acquisition. The elimination of this type of artifact using various signal processing approaches is considered a preprocessing task for different neural information processing applications. In this article, the wavelet domain optimized Savitzky–Golay (WOSG) filtering approach was proposed for the removal of motion artifacts from EEG signals. The multiscale analysis of the EEG signals using discrete wavelet transform (DWT) produces subband signals at different scales. Motion artifact is a low-frequency artifact that appears in the approximation subband signal. The optimized SG filter was applied to the motion artifact intermixed approximation subband signal, and the cleaned approximation subband signal was evaluated based on the subtraction of the optimized SG filter output from the motion artifact intermixed subband signal. The filtered EEG signal was computed based on the addition of cleaned approximation subband signal with other subband signals of contaminated EEG. The proposed WOSG filtering approach was evaluated using EEG recordings from various publicly available databases. Measures, such as the mean absolute error in power spectral density (MAE-PSD) of $\delta$ -band between contaminated and cleaned EEG signal, $\Delta$ SNR, percentage change in correlation coefficients ( $\eta$ ), and mutual information (MI), were used to quantify the performance of the proposed filtering approach. The results revealed that the proposed WOSG filtering approach had superior denoising performance with the average $\Delta$ SNR, $\eta$ , and MAE-PSD values of 30.59 dB, 68.76%, and 0.0263 dB/Hz in comparison to the multiresolution total variation (MTV) ( $\Delta$ SNR as 29.12 dB, $\eta$ as 68.56%, and MAE-PSD as 0.0365 dB/Hz) and other existing methods. The approach had the average MI values of 4.152 and 4.103 and the average MAE-PSD values of 0.276 and 0.256 dB/Hz for $\delta$ -bands of ...