The invention relates to the technical field of neural
rehabilitation training, and discloses a neural feedback
rehabilitation training method and
system. According to the method, electroencephalogram, near-
infrared, myoelectricity and
heart rate variability signals are acquired based on a multi-
modal nerve-physiological
signal acquisition device, and a nerve-physiological
signal sequence is generated and input into a fusion
processing system. And carrying out denoising, artifact removal and
feature extraction on the data sequence to generate standardized
feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target,
reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction
system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural
state model and
algorithm are adopted, so that the intelligence and accuracy of
rehabilitation training are improved.