Electroencephalogram-electromyographic signal fusion recognition method based on deep learning

Abstract

The invention discloses an electroencephalogram-electromyographic signal fusion recognition method based on deep learning, and the method comprises the steps: carrying out the high-dimensional coding and sequence decoding of a preprocessed electroencephalogram signal through a recurrent neural network based on a deep self-attention mechanism, and obtaining an electroencephalogram classification feature matrix; performing intensity feature extraction on the preprocessed electromyographic signals to obtain electromyographic feature vectors; and performing feature fusion on the electroencephalogram classification feature matrix and the myoelectricity feature vector through an unsupervised sparse auto-encoder to generate a final instruction. According to the method, a multi-head self-attention mechanism and a coding-decoding model are combined and applied to the field of electroencephalogram feature extraction, so that the electroencephalogram classification and recognition precision is improved; the problem that the electromyographic signals have individual differences and position differences is solved by utilizing a self-adaptive method, and the action intensity is estimated by extracting the intensity characteristics of the electromyographic signals; and an unsupervised sparse auto-encoder is utilized to encode and decode different bioelectricity characteristics, and the fusion of electroencephalogram and myoelectricity characteristics is realized by an efficient and robust method.

Description

technical field [0001] The invention belongs to the technical field of feature extraction and fusion identification of EEG signals and EMG signals, in particular to an EEG-EMG signal fusion identification method based on deep learning. Background technique [0002] The EEG signal is an electrical signal collected and recorded on the scalp with a non-invasive flexible electrode. This electrical signal is formed by the summation of the postsynaptic potentials that occur synchronously in a large number of neurons during brain activity. It is the physiological activity of brain nerve cells in the cerebral cortex. or a general reflection of the scalp surface. When the human body imagines limb movements without actual limb movements, the activity between neurons generates electrical signals. When the energy of these signals accumulates beyond a certain threshold, EEG signals are generated. The EEG signals generated by motor imagery have event-related synchronization and With the ...

AI Technical Summary

Problems solved by technology

Traditional human-computer interaction systems are mostly single-channel interaction methods, such as voice or gestures, with simple interaction methods and single application fields

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