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Brain Informatics
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Brain Informatics
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A Dynamic Directed Transfer Function For Brain Functional Network Based Feature Extraction

Authors: Mingai Li; Na Zhang;

A Dynamic Directed Transfer Function For Brain Functional Network Based Feature Extraction

Abstract

AbstractDirected transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with $$\beta_{1}$$ β 1 (13–21 Hz) and $$\beta_{2}$$ β 2 (21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, $$\beta_{1} ,$$ β 1 , $${ }\beta_{2}$$ β 2 ) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.

Related Organizations
Keywords

Graph theory, QA76.75-76.765, Neurology, Motor imagery electroencephalogram, Cognitive Neuroscience, Computer applications to medicine. Medical informatics, R858-859.7, Feature extraction, Brain functional network, Directed transfer function, Computer software, Computer Science Applications

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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