The invention relates to a
myelin sheath repair auxiliary method based on neural
signal modeling. According to the method, multi-
modal neural
signal data such as electroencephalogram signals of a to-be-recovered individual are obtained and preprocessed, and a preprocessed
data set is formed; then, an individualized
time sequence modeling process is executed,
time sequence feature representation containing indexes such as the
nerve conduction speed
recovery rate, the phase consistency and the
neural pathway integrity is generated, and individualized calibration is carried out through a dynamic baseline updating mechanism. The calibrated feature representation is input to a pre-trained
deep learning inference engine. The
inference engine outputs a
myelin sheath repair index and an imbalance risk
score for representing a repair degree and predicting a future abnormal risk. An intervention parameter set is generated based on the indexes, the intervention parameter set comprises electrical stimulation intervention parameters, virtual
rehabilitation training task difficulty and
rehabilitation training rhythms, and a control instruction is further formed to configure a virtual
rehabilitation training environment and an electrical stimulation execution interface, so that personalized assistance and dynamic optimization of
myelin sheath regeneration and repair are realized.