this is the open access code for paper "A novel robust Student’s-t-based Granger causality for EEG based brain network analysis"
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Please install the GCCA Toolbox to supplement the corresponding files.
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Usage form: [ret] = GauStu_Granger_regress(X,nlags,STATFLAG) X d*N data, d is the dimension， and N is the time series length nlags is the model order.
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”GauStu_Granger_regress“ is used in the same way as ”GauStu_Granger_regress“
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attention! if you have any question, I am anticipant receiving your letter, please contact the email:gaitxh@foxmail.com.
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2023/03/26 16:15
Not only that, but we've also developed a robust approach to EEG data preprocessing," In "https://github.com/Gaitxh/FCCJIA-An-adaptive-joint-CCA-ICA-method-for-ocular-artifact-removal provides a fast and robust eye electric artifact removal techniques, In the "https://github.com/Gaitxh/ATICA-An-Adaptive-EOG-Removal-Method-Based-on-Local-Density-" is also a kind of robust eye electric artifact removal techniques, It also provides a threshold calculation strategy. I hope these works can help you.