Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

Zhibo Zhang; Sani Umar; Ahmed Y. Al Hammadi; Sangyoung Yoon; Ernesto Damiani; Claudio Agostino Ardagna; Nicola Bena; Chan Yeob Yeun

doi:10.1109/access.2023.3245813

arXiv · 2301.06923

Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

Zhibo Zhang·Sani Umar·Ahmed Y. Al Hammadi·Sangyoung Yoon·Ernesto Damiani·Claudio Agostino Ardagna·Nicola Bena·Chan Yeob Yeun

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Abstract

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub.

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Zhibo Zhang, Sani Umar, Ahmed Y. Al Hammadi, Sangyoung Yoon, Ernesto Damiani, Claudio Agostino Ardagna, Nicola Bena, Chan Yeob Yeun. 2023-01-17. Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals. https://doi.org/10.1109/access.2023.3245813

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