A Classification Method of Steady-state Visual Evoked Potentials Based on Empirical Mode Decomposition

Abstract

A classification method of steady-state visual evoked potentials based on empirical mode decomposition. First, the collected multi-channel steady-state visually evoked potentials (SSVEP) are decomposed into several sub-signals in different frequency ranges after multivariate empirical mode decomposition. ; Then according to the correlation coefficient between the sub-signal of the known classification label signal and the template signal, calculate the classification accuracy index corresponding to the sub-signal; then calculate the correlation coefficient between the sub-signal and the template signal in the unknown label signal, by The classification accuracy index is used as the selection weight of the sub-signal correlation coefficient to reconstruct the correlation coefficient between the original signal and the template signal; finally, the steady-state visual evoked potential (SSVEP) is classified according to the reconstructed correlation coefficient between the original signal and the template signal; On the basis of improving the signal-to-noise ratio of the steady-state visual evoked potential (SSVEP), the invention achieves a higher classification accuracy rate for the steady-state visual evoked potential (SSVEP).

Description

technical field [0001] The invention relates to the technical field of classification of steady-state visual evoked potential signals (electroencephalogram signals), in particular to a classification method of steady-state visual evoked potentials based on empirical mode decomposition. Background technique [0002] Brain-computer interface is a new type of human-computer interaction, which can control external devices by reading the brain neural activity information of the subject. In actual use, cortical electroencephalogram (EEG) is used as the main signal source in the brain-computer interface control system due to its advantages of high time resolution and convenient signal extraction. The brain-computer interface of event-related EEG signals such as motor imagery and P300 is the basic mode of the brain-computer interface. After continuous exploration and research, steady-state visual evoked potential signal (SSVEP) has become the most widely used EEG signal in the brai...

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Problems solved by technology

[0004] (1) The canonical correlation analysis algorithm regards the steady-state visual evoked potential signal as a single EEG signal, and will lose effective feature information in the process of filtering and other preprocessing operations, resulting in limited classification accuracy

[0005] (2) In many improved algorithms, there is a lack of prior knowledge on the selection and weight of the sub-signals of the steady-state visual evoked potential signal after frequency division, and a lot of factors will be added in the process of decomposing and reconstructing the sub-signals. Noise signal, unable to effectively achieve the purpose of improving the signal-to-noise ratio of the steady-state visual evoked potential signal

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