The invention discloses an original electroencephalogram deep learning classification method, which comprises the steps of extracting a test sample, converting the obtained test sample into a test input image, and inputting the obtained test input image into a deep learning model, and carrying out robustness feature extraction to obtain robustness features, and inputting the extracted robustness features into an extreme learning machine classifier to carry out classification analysis. The deep learning model is composed of a convolutional neural network and a long-short-term memory neural network. The convolutional neural network can perform feature extraction by using a convolutional layer, and does not need to repeatedly search proper features, thereby saving a lot of time. Meanwhile, the convolutional layer is used for feature extraction, the accuracy of electroencephalogram analysis can be improved by changing the size of a convolution kernel, operation is easy and convenient, the speed is higher, and the accuracy can reach 90% or above.
Original electroencephalogram deep learning classification method and application