Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal

Abstract

The invention provides a method for rapidly and automatically identifying and removing ocular artifacts in an electroencephalogram signal and belongs to the technical field of biological information and the method is mainly applied to a process of acquiring and preprocessing the electroencephalogram signal. The method comprises the following specific steps of: carrying out discrete wavelet transformation on an acquired multi-channel electroencephalogram signal and an electro-oculogram signal to obtain multi-scale wavelet coefficients; using the wavelet coefficients connected in series as an input for analyzing an independent component, and rapidly acquiring the independent component by using a negative entropy criterion-based Fast ICA (Independent Component Analysis) algorithm; identifying the ocular artifacts through a cosine method, performing zero resetting on the independent component, and projecting the other components through ICA inverse transformation and returning to all electrodes of an original signal; and finally obtaining the electroencephalogram signal for removing the ocular artifacts through inversion of the wavelet transformation. By utilizing the method for rapidly and automatically identifying and removing the ocular artifacts in the electroencephalogram signal, the problems that an ICA method is poor in discrete effect and low in convergence rate when beingapplied to noisy electroencephalogram signals are solved, and the function of rapidly and automatically identifying and removing the ocular artifacts in the electroencephalogram signal is realized.

Description

Technical field: [0001] The invention belongs to the technical field of electroencephalography (Electroencephalography, EEG) acquisition and preprocessing. Specifically, it relates to a fast automatic removal method of electrooculogram artifacts in EEG signals based on discrete wavelet transform (Discrete Wavelet Transform, DWT) and independent component analysis (Independent Component Analysis, ICA). Background technique [0002] EEG signal is a kind of bioelectrical signal that reflects brain activity, and plays an increasingly important role in the process of studying human brain function and diseases. However, EEG signals are very weak and have high time-varying sensitivity, and are easily interfered by irrelevant noise during acquisition, thus forming various EEG artifacts. These artifacts bring great difficulties to the analysis and processing of EEG signals. Electro-oculogram (EOG) is one of the most important interference components in EEG signals, it will randomly...

AI Technical Summary

Problems solved by technology

[0014] Aiming at the two shortcomings of independent component analysis ICA when removing electro-oculogram artifacts in EEG, the separation effect and speed of the algorithm are affected by the interference of various noises, and the electro-oculogram artifacts cannot be automatically identified, the present invention proposes An automatic removal method of EEG artifacts in EEG signals based on discrete wavelet transform and independent component analysis, that is, DWT-ICA, does not require manual intervention in the process of removing eye traces, and the method has strong anti-noise ability. The convergence speed is fast and the real-time performance is good. At the same time, the signal-to-noise ratio of the EEG signal is greatly improved, which provides a new idea for the online preprocessing of the EEG signal.

Images