Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke - Wang, Zhongpeng; Cao, Cong; Chen, Long; Gu, Bin; Liu, Shuang; Xu, Minpeng; He, Feng; Ming, Dong (Frontiers in Neuroscience, 2022)
Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for stroke patients in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. motor imagery based brain computer interface (MI-BCI) combined with functional electrical stimulation(FES) is a potential active neural rehabilitation technology for stroke patients recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multi-modal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory and tactile multi-sensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after four weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns and cerebral oxygen response of stroke patients were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation.