A decoding method of motor imaginary EEG signals based on OA-WMNE brain source imaging

Abstract

A decoding method of motor imaginary EEG signals based on OA-WMNE brain source imaging is disclosed. The method includes firstly adopting baseline correction and superposition averaging in time domainto preprocess the EEG signals and then obtaining the superposition averaging signals of each motor imagery task; utilizing a WMNE algorithm to transform it into brain source space to obtain the dipole estimation; determining the time interval of interest (TOI) according to the difference of waveform between the two motion imagery dipoles; subjecting all single motion imaginary EEG signals to inverse transformation and forming all dipole amplitudes at each sampling point in TOI into eigenvectors to obtain a group of features at the sampling point; forming all the features on the sample pointsinto feature sample sets, which are normalized by zero-mean value, and reducing the feature dimension by using the univariate feature selection method; and finally, utilizing a support vector machineto classify the features so that the highest average decoding accuracy is obtained, the spatial resolution of EEG is improved, which is helpful to improve the decoding accuracy of motion imagination task.

Description

technical field [0001] The invention belongs to the technical field of brain source space decoding of EEG signals, in particular to a decoding method for motor imagery EEG signals from the brain dipole source space in a Brain Computer Interface (BCI) system, which adopts superposition in the time domain A method combining Overlapping Averaging (OA) and Weighted Minimum Norm Estimates (WMNE) brain-source imaging techniques (denoted as OA-WMNE) decodes motor imagery EEG signals in the brain-source spatial domain. Background technique [0002] Motor Imagery Electroencephalography (MI-EEG) hides a large amount of biological information in the motor perception cortex of the brain. MI-EEG signals recorded non-invasively in the scalp provide an important reference for brain activity in the field of sensors, because they have relatively High time-frequency resolution is widely used in the fields of BCI system research and clinical rehabilitation evaluation, so the salient features o...

AI Technical Summary

Problems solved by technology

However, this method has the following problems in practical application: (1) The preprocessing process of ICA to decompose the EEG signal will cause the loss of some effective information of the original MI-EEG, which is different from the purpose of enlarging the characteristic information of the scalp signal through the inverse EEG transformation. conflict

(2) The inverse transformation of a single independent component only maps a main source signal of the scalp electrode, and does not make full use of all the physiological information contained in MI-EEG. Uniform phenomenon, affecting the accuracy of decoding

(3) The selection of the brain-source spatial activation region (Region of Interest) is limited by the relatively obvious unilateral limb motor imagery with Event Related Desynchronization (ERD). For the correlation analysis of more complex motor imagery tasks , the results of the most relevant independent component source imaging are difficult to reflect the information of the real activity of the cerebral cortex, resulting in a decrease in decoding accuracy

Images