Method for simultaneously removing multiple artifacts from single-channel electroencephalogram signals

Abstract

The invention discloses a method for simultaneously removing multiple artifacts from single-channel electroencephalogram signals. The method includes the following steps that, firstly, single-channelelectroencephalogram signals are decomposed through singular spectrum analysis, and a multi-dimensional signal component matrix is obtained; 2, a second-order blind identification method is used for blind source separation of the signal component matrix, and several independent source components are obtained; 3, the independent source components containing the artifacts are selected and subjectedto zero setting; 4, the independent source components obtained after zero setting are subjected to blind source separation inverse transformation, and the clean single-channel electroencephalogram signals are obtained after reconstruction. The method achieves simultaneous removal of the artifacts (myoelectricity, electro-oculogram and electrocardio artifacts) from the single-channel electroencephalogram signals, and electroencephalography components in the signals are effectively retained.

Description

technical field [0001] The invention belongs to the technical field of EEG signal processing, and in particular relates to a combination of singular spectrum analysis and second-order blind identification method to simultaneously remove various artifacts (myoelectricity, electrooculogram, and electrocardiogram) from single-channel EEG signals. The new method of trace) is mainly used in the preprocessing of EEG signals. Background technique [0002] Electroencephalogram (Electroencephalogram, EEG) is the overall response of the electrophysiological activities of brain nerve cells on the surface of the cerebral cortex or scalp, and contains rich physiological information. With the development of technology, the application of EEG signal is more and more extensive. Usually, when the EEG signal is collected by an EEG machine, the EEG signal is extremely susceptible to interference from various artifacts. Typical types of artifacts include electromyography (Electromyography, EM...

AI Technical Summary

Problems solved by technology

However, in reality, the EEG signal is easily interfered by multiple artifacts at the same time

Due to the large differences in the characteristics of each type of artifact, the previously proposed single-channel EEG artifact removal algorithm may not be suitable for the situation where the EEG signal is simultaneously interfered by multiple artifacts

In addition, in the most widely accepted single-channel EEG artifact removal algorithm combining EEMD and blind source separation, the EEMD method used also has certain limitations.

For example, EEMD will decompose the single-channel EEG signal into different frequency bands, but the frequency of the artifact signal is likely to overlap with the EEG signal for the most part.

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