EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

Abstract

Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient's brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient's overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography