Method for Analyzing EEG Signals of Schizophrenic Patients Based on Small-sample Learning
Currently many machine learning methods are built on the demand of large amount of data,but in real production life,sometimes it is difficult to obtain large amount of data.This paper pro-poses to apply the small sample learning technique on EEG signal analysis to enhance the classification accuracy and improve the analysis performance.The experiment is carried out by preprocessing the col-lected EEG signals,then using their high dimensional feature vectors outputted by the convolutional and pooling layers of the pre-trained model as the input data for feature extraction,and finally training the model with small-sample learning to achieve better classification or prediction results in the case of a small dataset size.The method combines the ideas of convolutional neural network and meta learning,and achieves fast adaptation to unlabeled data by training on a small amount of labeled data.The experimental results show that the method has better classification accuracy and generalization ability in the case of small samples,and compared with the traditional machine learning method,it has higher application value and can provide reference for the diagnosis of schizophrenia disease.
small sample learningEEG signalconvolutional neural networkclassification ac-curacyschizophrenia
万方数据
维普
