Forearm Bio-Medical Signal Processing - Shakya, Sahaj; Ranjitkar, Bipul (International Journal on Engineering Technology, 2024)
This research utilizes low-dimensional surface EMG and EEG data, obtained from the human arm using ECG electrodes, to analyze forearm muscle signals through a novel approach. Both EMG and EEG signals are employed side by side: EEG captures brain activity, particularly in the beta (13-30 Hz) and alpha (8-12 Hz) frequency ranges, while EMG focuses on muscle activity in the 20 Hz to 200 Hz range. Beta waves are associated with motor planning and voluntary movements, while alpha waves decrease during movement execution, indicating disengagement from a resting state. Event-related desynchronization (ERD) of alpha and beta waves is vital in understanding motor tasks, including forearm movements. Although EEG alone showed a poor response in tracking movement execution, EMG provided better performance, with higher frequencies reflecting more intense muscle contractions and motor unit activation. The combination of EEG and EMG improves the analysis of event-related potentials (ERPs) in timing tasks, enabling precise monitoring of both brain and muscle activity. However, coordinating both signals can be complex and often relies on user experience. Extensive experimentation confirms that using this dual approach is feasible and effective in replicating natural arm movements. The findings highlight important advancements in applying signal processing techniques to muscle activity analysis, providing new insights into the control of muscles and enhancing accessibility to modern assistive technologies.