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 matrix gray modeling Model, the model parameters are used as features, so that the effect of P300 classification and recognition is better improved, and the detection and recognition rate of P300 signal is improved while reducing the number of stimulation repetitions

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