Single-time P300 detection method based on matrix grey modeling

Abstract

The invention provides a single-time P300 detection method based on matrix grey modeling, belongs to the field of cognitive neuroscience and relates to a feature extraction and recognition detection method of an event related potential P300, in particular to a single-time P300 detection method based on matrix grey modeling in a grey theory.The method comprises the steps that 1, a original acquired electroencephalogram signal is preprocessed; 2, electrocardiographic lead combinations are selected, namely four electrocardiographic leads with most obvious top occipital region waveform differences are selected as optimal electrodes according to oscillograms of target stimulus and non-target stimulus of training set data; 3, segmented matrix grey modeling is conducted on the data of the four electrocardiographic leads, and model parameters are extracted to serve as feature vectors; 4, a Fisher ratio value method is utilized to perform optimal feature selection, and meanwhile the purpose of decreasing the number of feature vector dimensions is achieved; 5, a support vector machine classifier is utilized to classify feature vectors, and single-time P300 detection recognition is achieved.Experimental data tests show that the method can improve single-time P300 detection recognition rate, and the correct recognition rate can be further improved during less-time superposition.

Description

technical field [0001] The invention belongs to the field of cognitive neuroscience, and relates to a feature extraction and recognition detection method of event-related potential P300, in particular to a single P300 detection method based on matrix gray modeling in gray theory. Background technique [0002] P300 (also known as P3b) is an endogenous component that can reflect the advanced cognitive processing process. It is the largest late positive wave recorded in the scalp with a latency of about 300ms when the subject recognizes the "target stimulus". Evoked by visual, auditory and somatosensory stimuli. [0003] P300 potential is widely used in brain-computer interface system (BrainComputerInterface, BCI) and psychological polygraph research. The traditional P300 identification method adopts the time-domain coherent averaging method, and the background random noise can be effectively reduced by superimposing and averaging. This method can identify P300 very well, but...

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Problems solved by technology

[0008] In order to solve the defects of low accuracy and low practicability of the single P300 extraction method in the prior art, the present invention provides a P300 feature extraction method based on matrix gray modeling, which constructs the P300 signal through

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