6 results about "Test phase" patented technology

The invention discloses a scalp electroencephalogram feature extraction and classification method based on an end-to-end convolutional neural network, and the method comprises the steps: carrying out the data enhancement of training data, and enabling the enhanced training data to train the convolutional neural network; inputting the to-be-detected data into the convolutional neural network for feature extraction and classification. The feature extraction and classification method comprises the following steps: S1, filtering an original scalp electroencephalogram signal by using a band-pass filter to obtain signals xtheta, xmu and xbeta; S2, performing multi-scale time convolution and spatial convolution on the signals xtheta, xmu and xbeta respectively to extract features; s3, performing pooling operation on the feature map output by the convolutional layer; s4, after pooling, carrying out feature fusion, and then sending the feature fusion to a full connection layer to integrate the input abstract features; and S5, sending the output of the full connection layer to a softmax layer for classification. According to the method, a brand new data enhancement technology is applied in a training stage, data is input into a plurality of convolutional neural network branches for multi-scale convolution operation after passing through a filter bank in a test stage, the overfitting phenomenon is reduced, and the classification accuracy is improved.