Time-varying constraint electroencephalogram or magnetoencephalogram source tracing method based on functional magnetic resonance imaging

Abstract

The invention provides a time-varying constraint electroencephalogram or magnetoencephalogram source tracing method based on functional magnetic resonance imaging. The method comprises the steps thatan electroencephalogram or magnetoencephalogram signal is recorded and traced, an initial electroencephalogram or magnetoencephalogram source intensity value is obtained, and an electroencephalogram or magnetoencephalogram source intensity matrix is obtained on the basis; functional magnetic resonance imaging data is recorded, a functional magnetic resonance imaging activation image is obtained onthe basis of the functional magnetic resonance imaging data, and then a functional magnetic resonance imaging weight matrix is obtained; a covariance matrix of a source is obtained according to the functional magnetic resonance imaging weight matrix and the electroencephalogram or magnetoencephalogram source intensity matrix, and the covariance matrix of the source is set to be changed along withtime; the interactive communication effect and the area-under-curve of a normalization part are calculated according to the initial electroencephalogram or magnetoencephalogram source intensity value, and the covariance matrix of the source is corrected on the basis; source tracing is carried out according to the corrected covariance matrix of the source, a final electroencephalogram or magnetoencephalogram source intensity value is obtained, and source tracing is completed.

Description

technical field [0001] The invention relates to the fields of electroencephalogram, magnetoencephalogram and magnetic resonance imaging, in particular to a time-varying constrained electroencephalogram or magnetoencephalogram tracing method based on functional magnetic resonance imaging. Background technique [0002] Electroencephalography (Electroencephalography, EEG) or magnetoencephalography (Magnetoencephalography, MEG) has a very high time resolution, which can reach the order of milliseconds, and their signals are mainly derived from postsynaptic potentials, which are direct measurements of brain activity. . However, since the number of EEG electrodes or MEG sensors is much smaller than the number of current sources in the brain, it is necessary to add additional constraints to uniquely determine the location of brain neural current sources, and these additional constraints are often lacking in physiology. Based on. Using EEG or magnetoencephalogram signals to study ...

AI Technical Summary

Problems solved by technology

However, not all situations satisfy these two assumptions. For example: if there is a blood oxygen metabolism signal in an area, but the corresponding electromagnetic signal cannot be recorded, this situation may be due to the closed field generated by neural activity, which violates the The first hypothesis will result in the existence of additional sources of functional magnetic resonance imaging; if the electromagnetic signal generated by a certain neural activity can be recorded in the scalp but the blood oxygen metabolism signal cannot be measured, this situation may be due to the discharge of these neurons The time is too short to cause little blood oxygen consumption, which violates the second assumption and may lead to the existence of fMRI loss sources

[0006] Therefore, we need a better method to solve the problem of inaccurate traceability results caused by the mismatch between the two positions

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