The invention discloses a deep learning network architecture search method for an EEG signal classification task. The method comprises the following steps: collecting a certain amount of electroencephalogram signals of a user according to a specific brain-computer interface task, carrying out downsampling on the collected electroencephalogram signals, and then carrying out band-pass filtering; searching a deep learning network architecture by utilizing the processed electroencephalogram signals; and setting network architecture parameters, calculating optimal model internal learnable parameters on a training set, fixing the model structure to be unchanged, obtaining the internal learnable parameters by using a gradient descent method, and outputting a final network structure. The method has the advantages of being convenient to integrate and low in integration cost, a network architecture needs to be artificially designed in advance in classical deep learning-based electroencephalogram interface system development, the method can change the artificial design process, and automatic design is completed by using a deep learning network, so the development process of the existing brain-computer interface system does not need to be greatly modified, and the system can be conveniently integrated into the existing system.
Deep learning network architecture search method for EEG signal classification task