Comparative Analysis of Various Filtering Techniques for Denoising EEG Signals

Electroencephalography (EEG) provides diagnostic information related to various brain disorders. Various types of interferences, like line interference, EOG, and ECG, muscle movement, cause artifacts in EEG data. Therefore, denoising EEG data plays a vital role in preserving the specific frequency content of the signal. Several filtering techniques are available to detach the noise to preserve the integrity of EEG signals. In this paper, we have compared different filtering techniques i.e., Adaptive filters, LPF Butterworth filter, Notch filter, wavelets on epileptic EEG signals, and sleep EEG signal. Our result suggests that the wavelet transform is the best option for denoising the EEG signal as it is more efficient in denoising the EEG signal without losing the original information. To select the best suitable wavelet function for denoising, Symlet4, Haar, Daubechies4, Biorthogonal2.6, Coiflets3, Discrete Meyer, Reverse Biorthogonal 6.8, Reverse Biorthogonal 2.8 has been used, and it is observed that wavelet function Bio-orthogonal 2.6 is the best suitable for denoising of EEG signal. Finally, a comparison between different filters has been done by two parameters MSE, PSNR. After a comparative analysis, we conclude that a wavelet transform is a useful tool than other filtering techniques in noise removal while sustaining diagnostic information in both the signal.

Automated summary

This paper compares different filtering techniques for denoising EEG signals, and finds wavelet transform to be the most effective option in preserving signal frequency content and removing noise.