Pioneering EEG Motor Imagery Classification Through Counterfactual Analysis

• URL: http://arxiv.org/abs/2312.09456v1
• Date: Fri, 10 Nov 2023 08:22:09 GMT
• Title: Pioneering EEG Motor Imagery Classification Through Counterfactual Analysis
• Authors: Kang Yin, Hye-Bin Shin, Hee-Dong Kim, Seong-Whan Lee
• Abstract summary: We introduce and explore a novel non-generative approach to counterfactual explanation (CE) This approach assesses the model's decision-making process by strategically swapping patches derived from time-frequency analyses. The empirical results serve not only to validate the efficacy of our proposed approach but also to reinforce human confidence in the model's predictive capabilities.
• Score: 26.859082755430595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of counterfactual explanation (CE) techniques in the realm of electroencephalography (EEG) classification has been relatively infrequent in contemporary research. In this study, we attempt to introduce and explore a novel non-generative approach to CE, specifically tailored for the analysis of EEG signals. This innovative approach assesses the model's decision-making process by strategically swapping patches derived from time-frequency analyses. By meticulously examining the variations and nuances introduced in the classification outcomes through this method, we aim to derive insights that can enhance interpretability. The empirical results obtained from our experimental investigations serve not only to validate the efficacy of our proposed approach but also to reinforce human confidence in the model's predictive capabilities. Consequently, these findings underscore the significance and potential value of conducting further, more extensive research in this promising direction.

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