Evaluating Robustness and Detection of Adversarial Attacks in EEG-Based Brain-Computer Interfaces - Costa, Beatriz C. da; Riker, André; Immich, Roger; Dalmazo, Bruno L. (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2026)
Research Context: Brain-computer interfaces (BCI) are systems that capture brain signals through techniques such as electroencephalography (EEG), processing these signals for various applications, especially in the control of devices for people with motor limitations. Despite the benefits, there are security concerns, including adversarial and cybersecurity attacks. Due to the emergence of brain-computer interaction devices on the market, it is necessary to analyze the security of these devices. Scientific and/or Practical Problem: Machine learning classifiers used in BCIs are vulnerable to adversarial attacks, which can compromise accuracy, safety, and user privacy. The lack of systematic evaluation of these vulnerabilities represents a gap in current research. Proposed Solution and/or Analysis: This work aims to emulate and analyze adversarial attacks in classifiers of BCI devices. Our experiments used the Foolbox tool to evaluate different adversarial techniques, such as DeepFool, FGSM, PGD, and Carlini-Wagner. Our evaluation identifies the negative effects of adversarial attacks on data classification. Related IS Theory: Technology acceptance model; Information processing theory. Research Method: Experiments were conducted on the BCI Competition 2008 Graz dataset, with attacks emulated during inference. Detection mechanisms based on Random Forest, SVM, and KNN were trained and evaluated to assess the feasibility of automatic defense. Summary of Results: Classifier accuracy decreased sharply under attack, with success rates ranging from 75.2% to 100%. Detection models achieved 83% accuracy with Random Forest and SVM for FGSM attacks, but only 5% with KNN for DeepFool, highlighting the challenge of detecting subtle perturbations. Contributions and Impact to IS area: The work demonstrates the vulnerabilities of BCI classifiers, proposes an evaluation pipeline for adversarial robustness, and emphasizes the importance of integrating security assessment into BCI development. Results have direct implications for information systems dealing with sensitive biomedical data.