An EEG feature extraction and classification method combining dae and cnn

Abstract

The present invention claims to protect a method for extracting and classifying EEG signal features combined with a noise reduction automatic encoding machine and a convolutional neural network. The method includes the steps of: collecting EEG data through an EEG signal acquisition instrument; removing the collected data Preprocessing such as abnormal samples, de-meaning, signal filtering, etc.; use the automatic encoding machine with noise coefficient to train the EEG signal; output the hidden layer of the noise reduction automatic encoding machine as feature data; and then convert the obtained feature data into similar Image format; use convolutional neural network for classification; finally use the test data set to test the performance of the trained network. Compared with other traditional methods, the present invention can obtain higher classification accuracy and stronger robustness.

Description

technical field [0001] The invention belongs to a method for feature extraction and classification of electroencephalogram signals, in particular to a method for feature extraction and classification of electroencephalogram signals combined with a noise reduction automatic encoding machine and a convolutional neural network. Background technique [0002] Brain-computer interface (BCI) is independent of peripheral nerve tissue and directly establishes a communication channel with external devices. Since it was first proposed, it has become a research hotspot in the fields of brain science and cognitive science. In a brain-computer interface system, signal recognition usually includes three parts: preprocessing, feature extraction, and classification. [0003] In the traditional method, in terms of preprocessing: using wavelet transform, ICA processing, spatial filtering and other methods, the present invention carries out signal preprocessing in three steps with reference to ...

AI Technical Summary

Problems solved by technology

In terms of feature extraction: common spatial pattern (CSP) is used to extract features of motor imagery, but time domain analysis is too expensive and requires a high number of EEG channels; autoregressive model method (AR) is used for prediction, but the AR model is suitable for Single-channel data has limitations for complex and high-dimensional EEG signals, and the classification accuracy is not high

In terms of classification methods: use linear discriminant analysis (LDA), but LDA is suitable for linear samples, not suitable for the nonlinear EEG data mentioned in this article; use support vector machine (SVM), SVM can better solve complex nonlinear data, but as a supervised network, labels are required in the training and testing process, and parameter adjustment is complicated

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