A Steady State Visual Evoked Potential Signal Classification Method Based on Convolutional Neural Network

Abstract

A method for classifying steady-state visual evoked potential signals based on convolutional neural networks. Firstly, checkerboard stimuli with different frequency flipping motions are presented to the user at the same time, and EEG acquisition equipment is used to collect EEG signals when the user gazes at a specific target. ; Then the original multi-channel EEG signals when the user looks at different stimulus targets are made into a labeled data set, and the data set is divided into a training set, a verification set and a test set; and then the training set is input into the designed depth volume The convolutional neural network model is trained, and the verification set is used to select the optimal parameters of the network. Finally, the test set is input into the trained deep convolutional neural network model to complete the identification of the stimulus target; the invention can realize the steady-state visual evoked potential The precise identification of signals has the characteristics of adaptively extracting signal features, does not require manual preprocessing, and can better adapt to individual differences through data learning.

Description

technical field [0001] The invention relates to the technical field of steady-state visual evoked potential brain-computer interface, in particular to a method for classifying steady-state visual evoked potential signals based on a convolutional neural network. Background technique [0002] Brain-computer interface (brain-computer interface, BCI) is a technology that does not depend on the normal output pathway of the brain, but directly realizes the communication between the brain and external devices such as computers. A means of communicating and controlling the external environment, such as manipulating a wheelchair through brain ideas. Commonly used brain-computer interface signal types include steady-state visual evoked potential (SSVEP), motor imagery, P300, etc. Among them, SSVEP has the advantages of strong stability and simple operation, and has become a widely used Brain-computer interface input signal. [0003] SSVEP is the response of the brain's visual system...

AI Technical Summary

Problems solved by technology

These traditional signal processing methods generally require a long period of visual stimulation to achieve better classification results, resulting in low recognition efficiency; and these methods use manual feature extraction to easily lead to information loss. Using the same recognition method for different users, Individual differences are not considered, so the recognition accuracy is low, which limits the engineering application of SSVEP-BCI

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