A novel robust Student's t-based Granger causality for EEG based brain network analysis

Gao，Xiaohui1; Huang，Weijie1; Liu，Yize1; Zhang，Yinuo1,2; Zhang，Jiamin1,3; Li，Cunbo3; Chelangat Bore，Joyce4; Wang，Zhenyu5; Si，Yajing6; Tian，Yin1; Li，Peiyang1

2023-02-01

10.1016/j.bspc.2022.104321

Biomedical Signal Processing and Control   Impact Factor & Quartile

1746-8094

1746-8108

Granger-causality-based brain network analysis has been widely applied in EEG-based neuroscience researches and clinical diagnoses, such as motor imagery emotion analysis and seizure prediction. However, how to accurately estimate the causal interactions among multiple brain regions and reveal potential neural mechanisms in a reliable way is still a great challenge, due to the influence of inevitable outliers such as ocular artifacts, which may lead to the deviation of network estimation and the decoding failure of the inherent cognitive states. In this work, by introducing Student's t-distribution into multivariate autoregressive (MVAR) model, we proposed a novel Granger causality analysis to suppress the outliers influence in directed brain network analysis. To quantitatively evaluate the performance of our proposed method, both simulation study and motor imagery EEG experiment were conducted. Through these two quantitative experiments, we verified the robustness of our proposed method to outlier influence when applying it to capture the inherent network patterns. Based on its robustness, we applied it for EEG analysis of emotions and assessed its efficiency in offering discriminative network structures for emotion recognition and discovered the biomarkers for different emotional states. These biomarkers further revealed the network-topology differences between male and female subjects when they experienced different emotional states. In general, our conducted experimental results consistently proved the robustness and efficiency of our proposed method for directed brain network analysis under complex artifact conditions, which may offer reliable evidence for network-based neurocognitive research.

Brain-network Estimation EEG Emotion analysis Granger Causality Analysis Student's t-distribution

SCI

English

Others

National Natural Science Foundation of China[61901077];Hainan Normal University[619QN260];National Natural Science Foundation of China[62171074];

WOS:000878751500002

2-s2.0-85140454669

Scopus

1.School of Bioinformatics,Chongqing University of Posts and Telecommunications,Chongqing,400065,China
2.Shenzhen Key Laboratory of Smart Healthcare Engineering,Department of Biomedical Engineering,Southern University of Science and Technology,Guangdong,518055,China
3.School of Life Science and Technology,Center for Information in Medicine,University of Electronic Science and Technology of China,Chengdu,610054,China
4.Department of Neuroscience,Learner Research Institute,Cleveland Clinic,Cleveland,44106,United States
5.Department of Network Engineering,Hainan College of Software Technology,Qionghai,571400,China
6.School of Psychology,Xinxiang Medical University,Xinxiang,453000,China

Gao，Xiaohui,Huang，Weijie,Liu，Yize,et al. A novel robust Student's t-based Granger causality for EEG based brain network analysis[J]. Biomedical Signal Processing and Control,2023,80.

Gao，Xiaohui.,Huang，Weijie.,Liu，Yize.,Zhang，Yinuo.,Zhang，Jiamin.,...&Li，Peiyang.(2023).A novel robust Student's t-based Granger causality for EEG based brain network analysis.Biomedical Signal Processing and Control,80.

Gao，Xiaohui,et al."A novel robust Student's t-based Granger causality for EEG based brain network analysis".Biomedical Signal Processing and Control 80(2023).