Real-Time Brain Signals Monitoring System Using Single-Channel EEG for BCI Applications

This study presents the development of a system for real-time monitoring of brain signals using a wearable device equipped with a single-channel EEG sensor. Brain signals, acquired through a custom-designed data acquisition protocol, are used to monitor cognitive and physiological states during various events. The labeled data are utilized to train a deep learning model. EEG signals are processed using a microcontroller and visualized using a graphical user interface (GUI). Data collection from participants was conducted under three different conditions: eyes open, eyes closed, and motor imagery tasks involving hand opening and closing. These conditions serve as training inputs for a deep learning model, enabling it to learn and classify signal patterns associated with different events. The acquired signals are analyzed by extracting various features and visualizing time-frequency domain properties for class differentiation. This system holds potential applications in cognitive and physiological monitoring and applications in brain-computer interface (BCI), such as prosthetic arm control, with the added benefits of low-power, cost-effective edge computing and green computing for sustainable deployment.