Multi-channel electroencephalogram signal eye artifact automatic removal method and storage medium

Abstract

The invention relates to a multi-channel electroencephalogram signal eye artifact automatic removal method and a storage medium, and the method comprises the steps: S1, processing an original EEG signal through FastICA and a differential evolution algorithm, so as to obtain a plurality of independent components; s2, processing the independent component containing the ocular artifacts by using wavelet transform and the differential evolution algorithm to obtain an ocular artifact component; and S3, based on wavelet reconstruction and inverse transformation, according to the ocular artifact component, obtaining an EEG signal without ocular artifacts. The invention relates to a method for automatically removing ocular artifacts by combining a differential evolution algorithm, FastICA and wavelet transform. According to the method, wavelet decomposition is adopted to decompose artifact components in FastICA, artifacts can be further separated out, useful information in EEG signals is reserved to the maximum extent, and the problem that the useful information is lost in the automatic artifact removing process is solved.

Description

technical field [0001] The invention belongs to the technical field of electroencephalogram signals, and in particular relates to a method for automatically removing electrooculogram artifacts of multi-channel electroencephalogram signals and a storage medium. Background technique [0002] Electroencephalogram (Electroencephalogram, EEG) technology is a relatively mature and typical non-invasive brain-computer interface (Brain-computer Interface, BCI) technology, and it is also a hot research field of BCI technology. EEG signals contain a wealth of information about brain activity and behavioral cognition, and are often used in brain activity analysis and disease diagnosis. However, EEG signals are easily polluted by various artifacts during the acquisition process. The pollution of artifacts is the most serious, which leads to the distortion of the collected EEG signals, which will reduce the accuracy of signal analysis and identification. Therefore, it is very important to...

AI Technical Summary

Problems solved by technology

However, the traditional ICA method needs to manually identify the components containing electrooculogram artifacts, which is very energy-consuming, and there are relatively large subjective factors, which affect the effect of artifact removal; in addition, ICA cannot completely separate the source signal, and the identified electrooculogram artifacts The trace components will contain some useful EEG information. If these artifact components are directly removed, a large amount of useful information will be lost.

[0004] The automatic removal of oculograph artifacts needs to set the threshold for identifying artifacts. At present, the commonly used thresholds include sample entropy, fuzzy entropy, autocorrelation coefficient, etc. However, there is no clear method for setting the threshold value. Subjective factors lead to low recognition accuracy of artifact components, which cannot guarantee optimal performance of artifact removal methods

[0005] Therefore, there is a loss of useful information in the process of automatic removal of multi-channel electro-oculogram artifacts, and the difficulty in identifying artifact components has become an urgent problem to be solved

Images