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

arXiv: https://arxiv.org/abs/2301.06923 · HTML: https://mlsanity.com/paper/2301.06923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Label Sanitization against Label Flipping Poisoning Attacks](https://mlsanity.com/paper/1803.00992/) (2018)
- [Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection](https://mlsanity.com/paper/1802.03041/) (2018)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach](https://mlsanity.com/paper/1706.04146/) (2017)
- [EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks](https://mlsanity.com/paper/2011.00101/) (2020)
- [Toward the application of XAI methods in EEG-based systems](https://mlsanity.com/paper/2210.06554/) (2022)
- [New data poison attacks on machine learning classifiers for mobile exfiltration](https://mlsanity.com/paper/2210.11592/) (2022)

## Built on by (most cited first, 3 of 3)

- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence](https://mlsanity.com/paper/2402.07915/) (2024)
- [Explainable Label-flipping Attacks on Human Emotion Assessment System](https://mlsanity.com/paper/2302.04109/) (2023)

## Abstract (arXiv)

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.