ABSTRACT: Electromyogram (EMG) contamination has been shown to affect electroencephalogram (EEG) signals. Therefore, methods of isolating and removing EMG contamination are a focus of research. One of the most common ways to eliminate this contamination is through independent component analysis (ICA). Also, surface Laplacian (SL) has been proven to isolate the distant sources of EEG signals. The objective of this paper is to demonstrate the effects of EMG contamination on EEG signals using the Neurophysiological Biomarker Toolbox (NBT) and the impact of applying ICA, and ICA + SL on raw data. In this paper, the method for preparing the data is ICA with an auto-pruned method and SL using a flexible spherical spline. Machine learning was used to classify three......
Key Word: Electromyogram(EMG); electroencephalogram (EEG); Laplacian (SL); Machine learning; frequency bands
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