Magnetoencephalogram source locating device based on sensor array decomposition and beam forming

Abstract

The invention discloses a magnetoencephalogram source locating device based on sensor array decomposition and beam forming. The device comprises a magnetoencephalogram sensor and a locating unit, the locating unit comprises an iterative decomposition module of a sensor array, a vector beam forming estimator module and a depth source locating and calculating module; the iterative decomposition module of the sensor array uses the iterative CP (CANDECOMP / PARAFAC) matrix decomposition method to reconstruct MEG sensor array signals; the vector beam forming estimator module uses reconstructed sensor array signals and second order statistical magnitude of samples to estimate the covariance matrix of the population; the depth source locating and calculating module estimates the location of a deep brain source by using the minimum variance optimization criterion of projection vectors. The technical scheme is less affected by the noise and interference of the sensor signals, the locating precision of the depth source is very high, and the method of finding the lesion area for epileptic patients of the origin of the medial temporal lobe can be better improved.

Description

technical field [0001] The invention belongs to the intersecting technical field of imaging technology in the field of biomedical information and lesion area positioning in medical neurology, and in particular relates to a magnetoencephalogram source positioning device based on sensor array decomposition and beam forming. Background technique [0002] Magnetoencephalography is a functional neuroimaging technique with high temporal and spatial resolution, and the signals acquired by the sensor are hardly affected by interference from the cranium and scalp. Therefore, using this high-quality sensor signal to deduce the location of the signal source in the brain, this source location device and method have been widely used in many fields such as cognitive neuroscience and neurology. One of the very important applications is to find epilepsy Patient's epileptogenic focus. According to the available data, magnetoencephalography has achieved good results in the localization of pa...

AI Technical Summary

Problems solved by technology

The reason why this type of method has a very good positioning effect is that it is mainly applied to the covariance matrix estimation and the minimum variance optimization criterion of the projected vector. However, the variance estimation using the original sensor data has disadvantages such as noise interference.

Mainly due to signals from deep or deeper sources in the brain being picked up by external sensors, these signals are inherently weak and some are not even easily observed

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