Electroencephalogram motion intention recognition method and system

Abstract

The invention provides an electroencephalogram motion intention recognition method and system. The method comprises the steps: obtaining an electroencephalogram data set based on a motion intention; after the electroencephalogram data set is subjected to feature extraction of at least two different dimensions, splicing and classifying the obtained features of all the dimensions, obtaining a motion intention recognition model with the highest classification accuracy, and outputting a classification result; the accuracy of motion intention classification and recognition can be effectively improved.

Description

technical field [0001] The invention relates to the application field of electroencephalogram signal recognition, in particular to a method and system for recognizing electroencephalogram motion intention. 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. A typical brain-computer interface system consists of an EEG signal extraction and acquisition module, an EEG signal processing module, an EEG signal output module, and an EEG signal feedback module. Commonly used EEG signal feature extraction methods are mainly divided into three categories. The first category is feature extraction methods for time domain, frequency domain, and time-frequency domain, mainly including fast Fourier transform, autoregressive model, wavelet transform and empirical Modal decomposition; the second category is spatial feature extraction methods, mainly includin...

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Problems solved by technology

[0004] In view of the above existing problems in the prior art, the present invention proposes a method and system for identifying EEG motion intentions, which mainly solves the problems of complex structure of EEG signal processing and low classification accuracy

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