Brain function network feature extraction method based on dynamic directional transfer function

Abstract

The invention discloses a brain function network feature extraction method based on a dynamic directional transfer function. The method mainly comprises the steps of firstly, performing preprocessingsuch as common average reference and lead optimization on an original motor imagery electroencephalogram signal; secondly, calculating a network connection edge of the preprocessed electroencephalogram signal by adopting a proposed DDTF algorithm, and respectively constructing brain function networks of different frequency bands; calculating network characteristic parameter outflow information andinformation flow gain according to the brain function network, and fusing two characteristic parameters in series to serve as characteristic vectors to be sent into a support vector machine for characteristic evaluation; and finally, determining an optimal parameter and an optimal frequency band according to a recognition rate closed loop to obtain a final classification result. The method is used for constructing the motor imagery brain function network, the network parameters obtained through calculation are used for MI-EEG feature extraction, the method not only can accurately describe change characteristics of MI-EEG in a frequency domain, but also accurately reflect a dynamic evolution process of BFN, and improvement of the MI-EEG classification accuracy is greatly facilitated.

Description

technical field [0001] The invention belongs to the field of motor imagery electroencephalogram (MI-EEG) feature extraction based on brain functional networks (brain functional networks, BFN), and specifically relates to: improving directed transfer function (directed transfer function, DTF), A dynamic directed transfer function (DDTF) method with variable order and variable frequency band is proposed. DDTF is further used to construct the brain function network and calculate its outflows and information flows. As features, MI-EEG features were classified using support vector machine (SVM). Background technique [0002] Brain-computer interface (Brain-computer interface, BCI) technology uses computers to establish a new way of external information exchange and control between the brain and external devices, and MI-EEG is often used in BCI systems. MI-EEG is a multi-lead time-frequency signal with spatial distribution characteristics. Complex motor imagery can activate diffu...

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Problems solved by technology

And because MI-EEG has individual differences, for different subjects, α frequency band (8-13Hz) and β frequency band (13-30Hz, including β 1 (13-21Hz) and β 2 (21-30Hz)) also vary greatly in activation strength

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