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In recent years, there has been an increased interest in using EEG and EMG signals to classify neuromuscular activity finding applications in BCI and prosthesis device control. However, accurate analysis and classification of these signals is greatly impacted by signal variability. The present study proposes an algorithm based on Scalograms to overcome these limitations associated with integrated classification of EMG and EEG signals for hand movements. Present work compared the application of four types of scalograms: shannon, frequency b spline, mexican hat, and complex gaussian for classification purpose. Among these scalograms the shannon scalogram provided the most accurate 97.40% results in distinguishing between open and closed hand movements. The findings have significant potential for applications such as assistive technology, brain-computer interfaces, and motor rehabilitation.
Negi et al. (Wed,) studied this question.