Motor imagery identification method and system fusing CNN-BiLSTM model and probability cooperation

Abstract

The invention discloses a motor imagery identification method and system fusing a CNN-BiLSTM model and probability cooperation, belonging to the field of man-machine interfaces. The invention aims tosolve the technical problem of how to effectively extract features and how to quickly realize classification, and the method adopts the technical scheme that (1) the method captures and extracts time-space depth features in an EEG signal by merging a CNN model of a BiLSTM network, and inputs captured and extracted time-space depth features into a ProCRC classifier for classification, and uses testset data pairs to construct the CNN-BiLSTM model for performance evaluation to achieve intent recognition. The method comprises the following specific steps: S1, acquiring an electroencephalogram signal; S2, constructing a deep neural network; S3, establishing a classifier; and S4, testing and evaluating the model. The system comprises an electroencephalogram signal acquisition unit, a deep neural network construction unit, a classifier construction unit and a model test and evaluation unit.

Description

technical field [0001] The present invention relates to the field of brain-computer interface, and more to the field of classification and recognition of electroencephalogram signals based on motor imagery, which combines a convolutional neural network and a bidirectional long-short-term memory model with a probabilistic cooperative representation classifier. A motion imagery recognition method and system integrating CNN-BiLSTM model and probabilistic collaboration. Background technique [0002] Brain-computer interface (Brain-Computer Interface, BCI) is a special communication system, it does not rely on the peripheral nerves and muscle tissue of the human body, it can realize the communication between the human brain and external equipment, this kind of communication does not require language or action control The communication method belongs to the category of brain control technology. In recent years, BCI technology has attracted the attention of many scholars at home a...

AI Technical Summary

Problems solved by technology

LSTM is improved on the basis of the traditional RNN model. By adding mechanisms such as forgetting gate, memory gate and output gate, it can effectively avoid the occurrence of gradient disappearance and blasting, and then learn more ancient information. However, simply Sometimes it is not enough to deduce the information from the previous information to the latter information. How to better use the LSTM model to extract the timing characteristics of the EEG signal in order to achieve better results is an urgent need to solve the technology in the current technology question

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