User-wise perturbations for user identity protection in EEG-based BCIs

Xiaoqing Chen ^1,2, Siyang Li ¹, Yunlu Tu ¹

⁰Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Journal of Neural Engineering +

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October 18, 2024

View abstract on PubMed

Summary

Protecting user identity in brain-computer interfaces (BCIs) is crucial. This study introduces novel privacy-preserving perturbations for electroencephalogram (EEG) data, effectively hiding identity without compromising BCI performance.

Area Of Science

Neuroscience
Computer Science
Biomedical Engineering

Background

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer direct brain-computer communication.
While BCI accuracy is widely studied, the ethical implications, particularly data privacy, are less explored.
EEG signals contain sensitive private information, including user identity, emotions, and health status, necessitating robust protection.

Purpose Of The Study

To investigate methods for protecting sensitive user identity information within EEG signals used for BCIs.
To develop and evaluate privacy-preserving techniques that prevent the unlearning of identity from EEG data.
To ensure that privacy enhancements do not negatively impact the core functionality of BCIs.

Main Methods

Proposed four novel user-wise privacy-preserving perturbation techniques: random noise, synthetic noise, error minimization noise, and error maximization noise.
Applied these perturbations to EEG training data to obscure user-specific identity markers.
Evaluated the effectiveness of perturbations using multiple neural network classifiers and traditional machine learning models across six diverse EEG datasets.

Main Results

Demonstrated that the proposed user-wise perturbations successfully render user identity information in EEG data unlearnable.
Confirmed that these perturbations do not interfere with or degrade the performance of the primary BCI task.
Validated the robustness and practical applicability of the developed privacy-preserving methods through extensive experiments.

Conclusions

It is feasible to effectively hide user identity information within EEG data.
Privacy-preserving perturbations can be implemented without compromising essential BCI task performance.
This research lays the groundwork for more ethical and secure development of BCI technologies.

Keywords:

brain–computer interface electroencephalogram privacy user identification