Method for improving performance of P300 spelling device

Abstract

The invention provides a method for improving the performance of a P300 spelling device. The method comprises the following steps: pre-processing a signal data set of a P300 spelling device to obtaina sample set; performing principal component analysis feature extraction on the sample set to obtain a feature set; identifying and classifying the feature set through a dynamic convergence differential neural network to obtain a plurality of neural network identification and classification output values; performing integrated averaging on the plurality of neural network identification and classification output values to obtain a signal classification result of the P300 spelling device; and combining the signal classification result of the P300 spelling device with a spelling interface of theP300 spelling device to obtain a final spelling character. According to the method, the parameter matrix is updated by using a neuro-dynamic formula, so that the convergence is better and the convergence speed is higher. According to the invention, the moving average filtering is adopted to optimize the signal, and the self-constructed dynamic convergence differential neural network is used to replace the original SVM classifier, so that the signal classification accuracy and processing speed are improved.

Description

technical field [0001] The invention relates to the field of brain electrical signal identification and control, in particular to a method for improving the performance of a P300 speller. Background technique [0002] In a brain-computer interface system, electroencephalogram (EEG) signals are applied to people with physical disabilities, brain injuries, or other movement disorders. Currently, noninvasive BCI systems are generally based on sensorimotor rhythm (SMR), slow cortical potential (SCP), and P300 event-related potential (ERP). Among the above signals, the P300 signal has a wide range of applications in BCI, such as spellers, neurofeedback training tools, and brain-controlled mobile platforms. The main challenges faced by the P300 signal in spellers are low signal-to-noise ratio, high dimensionality, classifier variability, and overfitting problems leading to difficulty in classification. [0003] In existing systems (S.Kundu and S.Ari,"P300 detection with brain-co...

AI Technical Summary

Problems solved by technology

The main challenges for P300 signals in spellers are low signal-to-noise ratio, high dimensionality, classifier variability, and overfitting issues leading to classification difficulties

[0003] In existing systems (S.Kundu and S.Ari,"P300 detection with brain-computer interface application using PCA and ensemble of weighted SVMs,"IETE Journal of Research,vol.64,no.3,pp.406–414,2018 .) The pre-processing uses the method of down-sampling to process the original signal, which can easily lead to signal distortion

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