Key result
Discrete Wavelet Transform (DWT) was proposed as an effective de-noising algorithm to extract power line and baseline artifacts from EEG signals and improve fidelity parameters like MSE, MAE, SNR, and PSNR.
Why the study?
Electroencephalogram signals have low amplitude and are corrupted by artifacts such as power line and baseline noise, which prevent accurate analysis unless removed.
The Discrete Wavelet Transform (DWT) is proposed as a method to remove power line and baseline artifacts from EEG signals to improve signal fidelity.
The nature of the electroencephalogram (EEG) signal is very fluid and spontaneous. Because of its lower amplitude, it is polluted by certain artifacts such as power line noise and baseline noise. Similarly, power line and baseline sounds have polluted electromyogram (EMG) and electrocardiogram (ECG) signals. These objects taint the initial signal's properties. Owing to the existence of objects, these signals cannot be accurately analyzed; hence, these sounds must be removed before analyzing the raw signal. The two important artifacts that corrupt a patient's EEG record are power line interference and baseline interference. This paper focuses on extracting power line and baseline artifacts from EEG signals using an effective de-noising algorithm called DWT (Discrete Wavelet Transform) to improve the signal's efficiency by increasing fidelity parameters including MSE, MAE, SNR, and PSNR. The MATLAB simulation is to be used for the implementation.
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et. al. S.Thenappan (2021) studied EEG signal artifacts. Discrete Wavelet Transform (DWT) de-noising algorithm was evaluated on Signal efficiency (fidelity parameters including MSE, MAE, SNR, and PSNR). Discrete Wavelet Transform (DWT) was proposed as an effective de-noising algorithm to extract power line and baseline artifacts from EEG signals and improve fidelity parameters like MSE, MAE, SNR, and PSNR.
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