# White-Box Target Attack for EEG-Based BCI Regression Problems

arXiv: https://arxiv.org/abs/1911.04606 · HTML: https://mlsanity.com/paper/1911.04606/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking](https://mlsanity.com/paper/2105.02480/) (2021)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs](https://mlsanity.com/paper/2411.02094/) (2024)
- [Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2212.00727/) (2022)
- [Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces](https://mlsanity.com/paper/2201.07711/) (2022)
- [Towards Privacy-Preserving Neural Architecture Search](https://mlsanity.com/paper/2204.10958/) (2022)
- [Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions](https://mlsanity.com/paper/2101.06704/) (2021)
- [On the Robustness of Decision-Focused Learning](https://mlsanity.com/paper/2311.16487/) (2023)
- [Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model](https://mlsanity.com/paper/2404.15373/) (2024)
- [Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks](https://mlsanity.com/paper/2505.00487/) (2025)
- [Making Brain-Computer Interfaces More Secure](https://mlsanity.com/paper/2606.02597/) (2026)
- [SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models](https://mlsanity.com/paper/2608.16931/) (2026)

## Abstract (arXiv)

Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning models are vulnerable to adversarial examples, which are crafted by adding deliberately designed perturbations to the original inputs. Many adversarial attack approaches for classification problems have been proposed, but few have considered target adversarial attacks for regression problems. This paper proposes two such approaches. More specifically, we consider white-box target attacks for regression problems, where we know all information about the regression model to be attacked, and want to design small perturbations to change the regression output by a pre-determined amount. Experiments on two BCI regression problems verified that both approaches are effective. Moreover, adversarial examples generated from both approaches are also transferable, which means that we can use adversarial examples generated from one known regression model to attack an unknown regression model, i.e., to perform black-box attacks. To our knowledge, this is the first study on adversarial attacks for EEG-based BCI regression problems, which calls for more attention on the security of BCI systems.