Brain intention identification method and system based on brain-computer interface

Abstract

The invention provides a brain intention identification method based on a brain-computer interface. The method comprises the following steps: constructing an actual task model by utilizing MATLAB; performing an experiment and electroencephalogram information collection based on the actual task model to obtain an electroencephalogram original data set; performing data preprocessing on a plurality of electroencephalogram data in the electroencephalogram original data set to obtain a feature extraction matrix; inputting the electroencephalogram signal feature extraction matrix and a correspondinglabel into an extreme learning machine to obtain an extreme learning model; and inputting to-be-predicted electroencephalogram data into the extreme learning model to obtain a classification result.The accuracy of identifying the brain intention is improved. The features of the electroencephalogram signals collected by the method are more obvious after common spatial pattern feature extraction,and classification and identification are easier. Besides, the model trained by the extreme learning machine is used as a classifier so that accuracy is high, tedious iterative computation is not needed in the classification process, speed is higher, and effect is better.

Description

technical field [0001] The invention relates to the technical field of biological signal processing and machine learning, in particular to a method and system for recognizing brain intentions based on a brain-computer interface. Background technique [0002] Brain-computer interface analyzes brain intentions by extracting scalp EEG signals, and then evaluates brain activity, which is of great significance for solving the medical rehabilitation problems of patients with movement disorders. [0003] In recent years, experts and scholars at home and abroad have carried out a series of research on brain-computer interface. There are three key steps in the research on brain intention recognition: designing reasonable experimental tasks, extracting features of EEG signals, and classifying EEG data. Among them, the establishment of a reasonable experimental task model is the first prerequisite for EEG signal extraction. In the study of brain-computer interface, experts at home and ...

AI Technical Summary

Problems solved by technology

In the rehabilitation of the motor nervous system, motor imagery is often used to induce EEG. However, if the experiment is performed under pure motor imagery for a long time, the subjects are prone to brain fatigue

The quality of the experimental data collected in this state is not high, which brings great pressure to the extraction of EEG signal features, and also reduces the accuracy of brain intention recognition

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