Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers (2001.11569v4)

Abstract: An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g., amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e., they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

• The paper demonstrates that minimal adversarial perturbations can misdirect outputs in EEG-based BCI spellers for both P300 and SSVEP systems.
• The study applies gradient-based machine learning techniques to develop perturbation templates that reliably disrupt EEG signal classification.
• The findings underscore the urgent need for robust security measures in clinical BCI applications to prevent miscommunication in patient care.

Adversarial Perturbations in EEG-Based BCI Spellers: Vulnerabilities and Implications

The research paper by Zhang et al. presents a detailed investigation into the security vulnerabilities of Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) spellers. Specifically, it addresses how adversarial perturbations—small, deliberately crafted alterations to input signals—can destabilize the outputs of these systems, which are crucial for enabling communication in severely disabled individuals, such as those affected by amyotrophic lateral sclerosis (ALS). The paper's exploration into P300 and Steady-State Visual Evoked Potential (SSVEP) spellers fills a notable gap in BCI research, which has predominantly focused on improving accuracy and speed rather than security.

Methodological Approach and Key Findings

The paper employs rigorous machine learning techniques to construct adversarial perturbation templates that convert normal EEG signals into adversarial examples capable of misleading BCI systems in both P300 spellers and SSVEP spellers. This method leverages the concept of gradient directions to develop perturbations effective against classifiers in these BCI systems.

1. P300 Speller Vulnerability:
   • P300 spellers, employing oddball paradigms to elicit and identify P300 potentials, were shown to be highly sensitive to adversarial perturbations. The researchers generated perturbation templates that, when added to benign EEG signals, could misdirect system outputs almost regardless of the intended user input. This was validated with substantial success rates across different experimental settings, pointing to a critical vulnerability.
2. SSVEP Speller Vulnerability:
   • Similarly, SSVEP spellers, which utilize frequency recognition from the EEG to infer user intent, demonstrated susceptibility to adversarial attacks. The authors crafted perturbation templates which altered EEG signal properties, like stimulation frequency, such that the system misclassified user inputs. The effectiveness of these attacks varied among individuals but was notably potent in some cases.
3. Transferability and Persistence:
   • Alarmingly, these adversarial perturbations showed transferability potential, meaning templates generated for one set of conditions could potentially disrupt different models or scenarios. This raises concerns about the broad applicability of such attacks across various BCI systems and architectures.

Practical and Theoretical Implications

The implications of this research are profound. Practically, the findings raise the urgent need for enhanced security measures in EEG-based BCI systems, especially those deployed in clinical settings for communication purposes. The inadvertent or malicious manipulation of BCI outputs could lead to severe misdiagnosis or miscommunication with life-altering consequences for patients.

From a theoretical standpoint, this paper challenges the current paradigms of BCI research by shifting some focus toward the robustness and security of BCIs. The insights gathered here could guide the development of more resilient BCI systems against adversarial influences, thereby enhancing their reliability and trustworthiness.

Future Directions

The paper concludes by suggesting pathways for addressing these vulnerabilities, such as improving the adversarial robustness of BCI classifiers and exploring methods for detecting and nullifying adversarial perturbations in real-time. Future research could benefit from advancing defense strategies, perhaps drawing from frameworks in other machine learning domains where adversarial attacks have also been extensively studied.

In summary, this paper provides a crucial exposition of the vulnerabilities in EEG-based BCI spellers due to adversarial perturbations, urging a reevaluation of current security measures. As the adoption of these interfaces grows, integrating adversarial defense mechanisms will be essential for their safe and effective use.