Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery

In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (N...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2016-07, Vol.27 (7), p.1429-1444
Main Authors: Sanqing Hu, Hui Wang, Jianhai Zhang, Wanzeng Kong, Yu Cao, Kozma, Robert
Format: Article
Language:English
Subjects:
Brain modeling
Brain-Computer Interfaces
Causality
Cerebral Cortex
Classification
Connectivity
Electroencephalogram (EEG)
Electroencephalography
Estimates
Fatal
Frequency-domain analysis
Granger causality (GC)
Human-computer interface
Humans
Imagery
Linear regression
Mathematical model
motor imagery (MI)
Motors
Neural networks
new causality (NC)
Time series analysis
Time-domain analysis
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Summary:In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2441137