Granger Connectivity Analysis as a Block-Term Tensor Regression for eSport Players

Airat Kotliar-Shapirov; Sergei Gostilovich; Anastasia Sozykina; Anh Huy Phan; Andrzej Cichocki

ICASSP 2024accepted0 citations

Granger Connectivity Analysis as a Block-Term Tensor Regression for eSport Players

Airat Kotliar-Shapirov, Sergei Gostilovich, Anastasia Sozykina, Anh Huy Phan, Andrzej Cichocki

Abstract

We developed a new tensor-based technique for connectivity analysis and applied it to the EEG data of 10 professional eSports players and 10 novices (control group) collected during 4 different oddball paradigms. The proposed technique utilizes a low-rank approximation of the Granger Causal autoregression with a single temporal filter, thus reducing the number of parameters and improving the convergence rate. Results showed that the temporal filter converges to a Morlet wavelet and establishes a strong connection between channels in the parietal cortex and sustainable negative connectivity between the frontal and occipital cortex, which corresponds to visual search potentials. Professional players also had significantly more prominent and faster ERP responses, which is consistent with the previous research.

BibTeX

@inproceedings{icassp2024_grangerconnectiv,
  title = {Granger Connectivity Analysis as a Block-Term Tensor Regression for eSport Players},
  author = {Airat Kotliar-Shapirov and Sergei Gostilovich and Anastasia Sozykina and Anh Huy Phan and Andrzej Cichocki},
  booktitle = {ICASSP 2024},
  year = {2024}
}