Deep stack network-based electroencephalogram signal feature extraction and classification method

An EEG signal and stacking network technology, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of EEG signal recognition process analysis, consume a lot of manpower and material resources, and reduce the generalization ability of the model. The effect of reducing the gradient dispersion problem, improving the classification recognition rate, and facilitating parallel operations
CN106529476AActive Publication Date: 2017-03-22西安慧脑智能科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
西安慧脑智能科技有限公司
Publication Date
2017-03-22

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Abstract

The invention discloses a deep stack network-based electroencephalogram signal feature extraction and classification method. The method comprises the steps of firstly acquiring electroencephalogram signal data by using an Emotiv electroencephalogram signal acquisition instrument; performing preprocessing of mean removal, filtering, normalization and the like on electroencephalogram signals; then performing independent pre-training on electroencephalogram signals of a single channel by using a plurality of restricted Boltzmann machines, extracting the electroencephalogram signals of the single channel, and applying parameters obtained by training to parameter initialization of a neural network; finally performing micro-adjustment on the network by adopting a batch gradient descent method, and effectively fusing electroencephalogram signal features of all channels; and performing performance testing on the network and implementing classification. According to the method, relatively high classification accuracy can be obtained.
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Description

technical field

[0001] The invention relates to the technical field of feature extraction and classification methods of EEG signals, in particular to a method for feature extraction and classification of EEG signals based on a deep stack network. Background technique

[0002] Brain-computer interface (BCI) is a human-computer interaction method that directly communicates with computers or external devices through the human brain. BCI technology provides a new information exchange channel for paralyzed patients, can improve the quality of life of patients, and has great practical value in the medical field, cognitive science, psychology, military field, entertainment and wearable smart equipment fields.

[0003] The recognition of electroencephalogram signal (EEG) is the key technology of BCI, including signal preprocessing, feature extraction and feature classification. Commonly used EEG signal feature extraction methods include autoregressive (AR) model, wavelet transform,...

Claims

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