Discriminability of single-trial EEG during decision-making of cooperation or aggression: A study based on machine learning

Zhihua Huang, Kun Jiang, Jing Li, Wenxing Zhu, Huiru Zheng, Yiwen Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Decision-making is a very important cognitive process in our daily life. There has been increasing interest in the discriminability of single-trial electroencephalogram
(EEG) during decision-making. In this study, we designed a machine learning based framework to explore the discriminability of single-trial EEG corresponding to different decisions. For each subject, the framework split the decisionmaking
trials into two parts, trained a feature model and a classifier on the first part, and evaluated the discriminability on the second part using the feature model and classifier. A proposed algorithm and five existing algorithms were applied to fulfill the feature models, and the algorithm Linear Discriminative Analysis (LDA) was used to implement the classifiers. We recruited 21 subjects to participate in Chicken Game (CG) experiments. The results show that there exists the discriminability of single-trial EEG between the cooperation and aggression decisions during the CG experiments,
with the classification accuray of 75% ( 6%), and the discriminability is mainly from the EEG information below 40 Hz. The further analysis indicates that the contributions
of different brain regions to the discriminability are consistent with the existing knowledge on the cognitive mechanism of decision-making, confirming the reliability of the conclusions. This study exhibits that it is feasible to apply machine learning methods to EEG analysis of decisionmaking cognitive process.
Original languageEnglish
Number of pages14
JournalMedical and Biological Engineering and Computing
Publication statusAccepted/In press - 30 Mar 2022

Keywords

  • Discriminability of single-trial EEG
  • Adaptive frequency common spatial pattern
  • Decision-making
  • Chick game

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