Brain-computer interface (BCI) has been widely used to capture electrical signals generated from the brain. One of the most commonly used methods in the BCI system is the electroencephalogram (EEG). However, processing brain signals is challenging and requires a lot of computation processes. This study selected single-channel EEG as the input command for a portable soft exoskeleton glove system. This research aims to develop an affordable soft exoskeleton glove driven by single-channel EEG for people with impaired hand motion. We proposed an intuitive control method that feels more natural by imagining hand movements. Eighteen healthy participants underwent EEG data collection while their brain activity (attention level) was measured under four controlled conditions: listening to preferred music with lyrics, listening to disliked music, pre-workout state, and post-workout state. These variations in attention level, mood, and physical exertion influenced the measured EEG signals to drive the soft glove. T-test was applied to determine the significant difference for noise environment and physical variation tests. Those EEG signals are used to drive the linear actuator and provide mechanical assistance. Simple on/off control was embedded in the soft glove microcontroller to control the finger flexion/extension based on the EEG signal as a command. The result shows that the proposed wearable soft exoskeleton glove driven by EEG signal can be a potential assistive device for people with hand impairment. The speed for the soft glove was 3 seconds to close completely from a fully open. For optimal performance, this system needs to be used in a calm and distraction-free environment when the user is well-rested.
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