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The distortion of neural signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of neural data. State of the art electrophysiology employs band-stop filters with which powerline noise are attenuated to low-amplitudes. Due to the instability of neural signals, the distribution of signals filtered out may not be centered at \(50/60Hz\). As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw electrophysiological recording and subtract it from the raw data in order to obtain neural data. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a band-stop finite impulse response filter. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned band-stop finite impulse response filter, especially at low signal-to-noise ratio.
This preprint is a submission to PeerJ for review.