Electrode selection method based on brain-derived imaging and correlation analysis

A correlation analysis, electrode technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as weak correlation

Active Publication Date: 2019-12-20
BEIJING UNIV OF TECH
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Problems solved by technology

[0007] In view of the above shortcomings, the present invention proposes an electrode selection method based on ESI and correlation analysis, that is, the sLRC method, which uses brain source imaging and correlation analysis to select electrodes, and uses uniformly distributed basic electrode groups with high correlation The optimal electrode group is composed of a combination of electrodes with positive characteristics. On the basis of ensuring the uniform distribution of electrodes, electrodes with strong correlation with th

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  • Electrode selection method based on brain-derived imaging and correlation analysis
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  • Electrode selection method based on brain-derived imaging and correlation analysis

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Embodiment Construction

[0061] The concrete experiment of the present invention is carried out in the Matlab R2017a emulation environment under Windows 8 (64 bits) operating system.

[0062] The MI-EEG data set used in the present invention comes from the Data sets IVa public database of BCI Competition III, and is collected by developers using 118 electrodes uniformly distributed under the expanded 10-20 electrode system of international standards, and the sampling frequency is 100Hz. After 0.05-200Hz band-pass filtering. The distribution of electrodes on the scalp layer is as follows: figure 2 shown.

[0063] The timing diagram of the collection test is as follows: image 3 As shown, each experiment lasted 5.25s. 0-3.5s is the motor imagery period. When t=0s, arrows appear on the screen, representing the two motor imagery tasks of right hand and foot respectively. The subjects perform motor imagery according to the instructions on the screen; 3.5s-5.25s is rest During the period, the screen wa...

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Abstract

The invention discloses an electrode selection method based on brain-derived imaging and correlation analysis. The electrode selection method includes the steps that a base electrode set is determinedaccording to initial electrode configuration of an electrode cap; then collected motor imagery electroencephalogram signals are preprocessed by band-pass filtering; then a standardized low-resolutionbrain electromagnetic tomography imaging algorithm is used for brain-derived imaging of MI-EEG, and a time series of a brain-derived domain dipole amplitude is obtained; then according to a dipole amplitude peak of each experiment, a cortical activated area is determined, and the time series of the dipole amplitude in the activated area and Pearson correlation coefficients of the MI-EEG signals of each electrode are calculated and arranged in descending order; and finally, an optimal electrode set is formed by selecting an electrode with a high correlation coefficient to be combined with thebase electrode set. According to the electrode selection method, electrodes with weak correlation with an imaginary task and disadvantageous to classification are excluded, and great significance forimproving the computational efficiency and the experimental convenience is achieved.

Description

technical field [0001] The invention belongs to the field of electrode selection in a brain-computer interface (brain-computer interface, BCI) system, in particular to an electrode selection method based on the combination of EEG Source Imaging (ESI) and correlation analysis, using standardized low-resolution The sLORETA algorithm transforms the EEG signal in the sensor domain to the brain source domain, determines the activation area of ​​the cerebral cortex based on data-driven (Data-driven), and calculates the time series of dipole amplitudes in each electrode and activation area The correlation coefficient of the optimal electrode group selection. Background technique [0002] Brain-computer interface (BCI) system is a new mode of human-computer interface based on EEG signals. BCI does not rely on the normal output channels of human peripheral nerves and muscle tissue, but directly establishes a control pathway between the brain and external communication devices. With...

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Application Information

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IPC IPC(8): A61B5/0476A61B5/0478
CPCA61B5/7225A61B5/7203A61B5/7235A61B5/7267A61B5/291A61B5/369
Inventor 李明爱董宇欣张娜
Owner BEIJING UNIV OF TECH
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