Motor imagery electroencephalogram signal classification method based on deep learning

An EEG signal and motor imagery technology, applied in the field of pattern recognition and brain-computer interface, can solve the problems of slow speed, high requirements for EEG signal preprocessing, and insufficient classification accuracy, so as to reduce redundancy and improve classification Efficiency, fast learning effect

Inactive Publication Date: 2019-05-21
XI AN JIAOTONG UNIV
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Problems solved by technology

[0004] Traditional EEG signal classification methods mainly use power spectrum, wavelet transform and other methods to extract features combined with support vector machines and artificial neural networks to classify the extracted features, but the requirements for EEG signal preprocessing are relatively high, and the classification accuracy rate is relatively high. Not tall enough, slower

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  • Motor imagery electroencephalogram signal classification method based on deep learning
  • Motor imagery electroencephalogram signal classification method based on deep learning
  • Motor imagery electroencephalogram signal classification method based on deep learning

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Embodiment

[0050] see figure 1 , the classification method of the motor imagery EEG signal based on deep learning of the present embodiment, comprises the following steps:

[0051]Step 1: Acquisition of experimental data, that is, collecting the original EEG signal data set, the subject wears an electrode cap during collection, and the original EEG signal is performed through the 64-lead EEG amplifier of the 10-10 method calibrated by the International Electroencephalography Association Collect and select all electrode positions. Each subject will conduct 14 experiments. When collecting, the subject sits on a chair, and there is a screen in front of the subject, and there is a target on the screen: the first two experiments It is a basic experiment lasting 1 minute. The basic experiment includes opening and closing the eyes, that is, in the first two experiments, the eyes were opened for 1 minute and the eyes were closed for 1 minute; then there were three cycles of experiments, and each...

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Abstract

The invention discloses a motor imagery electroencephalogram signal classification method based on deep learning, and belongs to the technical field of mode recognition and brain-computer interfaces.The method comprises the following steps: analyzing and preprocessing electroencephalogram signals, carrying out two-dimensional conversion on the electroencephalogram signals, and constructing a convolutional neural model of motor imagery electroencephalogram signal classification; and training the neural network to obtain a motor imagery electroencephalogram signal classification model, and carrying out test and performance evaluation on the model. Compared with the prior art, the method has the advantages that the data of each time point are used as samples, 2D conversion and deep learningmethods are carried out, the classification accuracy of the motor imagery electroencephalogram signals is improved, and meanwhile the classification instantaneity and stability are improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and brain-computer interface, and specifically relates to a method for classifying motor imagery EEG signals based on deep learning. Background technique [0002] Brain-Computer Interface (BCI) is an emerging technology that establishes a communication bridge between the brain and external devices, enabling external devices to directly use signals in the brain to guide external activities, such as controlling prosthetics, Movement of electric wheelchairs, etc. Brain-computer interface technology has a wide range of applications, such as smart home, smart medical care, smart entertainment and other fields. The brain-computer interface based on motor imagery EEG signals is the main application of this technology. By analyzing the EEG signals of the brain performing imaginary movements, the brain state and activities can be identified to achieve the purpose of controlling external device...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F3/01G06K9/62G06N3/04G06N3/08
Inventor 吕娜井雪
Owner XI AN JIAOTONG UNIV
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