Classification and recognition method for P300 event-related potential based on deep learning

Abstract

The invention relates to a classification and recognition method for P300 event-related potential based on deep learning, and belongs to the technical field of medical and physiological signal detection and processing analysis. According to the invention, a Butterworth filter is adopted to successively perform high-pass filtering and low-pass filtering on original signals to remove artifacts and power frequency interference; the data is amplified by using a one-time superposition averaging technique, normalization and time domain truncation are performed on EEG signals, and corresponding supervisory signals are formulated according to the signal types; the EEG data is divided into a training set and a verification set after data preprocessing, a deep learning network capable of classifyingand recognizing the P300 event-related potential is constructed, and the feature extraction ability of the network is improved; the probability that an input signal contains the P300 event-related potential is finally predicted through the trained network; and finally, a target character is predicted according to an experimental paradigm and the probability value outputted by the network. The experiment shows that the algorithm is good in performance and can also obtain good character recognition accuracy under the condition of reducing the number of experiments.

Description

technical field [0001] The invention belongs to the technical field of medical and physiological signal detection, processing and analysis, and relates to a method for classifying and identifying P300 event-related potentials in EEG signals, in particular to a method for classifying and identifying P300 event-related potentials by using deep learning. Background technique [0002] In the character spelling brain-computer interface (BCI) system, the character spelling function is realized by detecting the P300 event-related potential in the EEG signal related to human cognition. If P300 event-related potentials can be classified and identified efficiently and accurately, it will be helpful for the practical application of the BCI system. Early studies used traditional signal processing and analysis methods to extract features such as the maximum amplitude difference and waveform area of ​​the signal and sent them to the classifier for classification and recognition. However, ...

AI Technical Summary

Problems solved by technology

Although the signal preprocessed by ICA is easier to identify accurately, but it takes a certain amount of time to preprocess the data

At present, some researchers are studying the recognition of P300 time-related potentials based on deep learning. Although the classification accuracy is better than that of traditional machine learning methods, the accuracy of classification and recognition still needs to be improved under the condition of reducing the number of experiments.

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