An adaptive brain-computer interface decoding method based on multi-model dynamic integration. A traditional state space model is improved, and a group of fixed functions rather than one fixed function is used to dynamically represent a relationship between an observation variable and a state variable; and a plurality of linear decoders and non-linear decoders are utilized, and the decoders are automatically switched according to data conditions during a decoding process of a brain-computer interface system, such that adaptive brain signal decoding is realized. By means of a multi-model time-sharing integration policy, the capabilities of linear decoders and non-linear decoders can be fused, such that the accuracy and stability of a brain-computer interface system are improved, and the problem of the decoding of a brain-computer interface system being unstable due to the unstability of a neural signal is solved to a certain extent.
ADAPTIVE BRAIN-COMPUTER INTERFACE DECODING METHOD BASED ON MULTI-MODEL DYNAMIC INTEGRATION