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

• URL: http://arxiv.org/abs/2001.11569v4
• Date: Thu, 16 Jul 2020 23:14:34 GMT
• Title: Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellers
• Authors: Xiao Zhang, Dongrui Wu, Lieyun Ding, Hanbin Luo, Chin-Teng Lin, Tzyy-Ping Jung, Ricardo Chavarriaga
• Abstract summary: An electroencephalogram (EEG) based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable.
• Score: 45.439697770189134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Professor X: Manipulating EEG BCI with Invisible and Robust Backdoor Attack [4.579605201643072]
  Professor X is an invisible and robust "mind-controller" that can arbitrarily manipulate the outputs of EEG BCI. Experiments on datasets of three common EEG tasks demonstrate the effectiveness and robustness of Professor X.
  arXiv  Detail & Related papers  (2024-09-30T10:10:52Z)
• Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces [28.686844131216287]
  Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs) Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. This paper, for the first time, explores adversarial detection in EEG-based BCIs.
  arXiv  Detail & Related papers  (2022-11-28T11:05:32Z)
• Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events [11.650381752104296]
  We explore the feasibility of manipulating EEG-based Motor Imagery (MI) Brain Computer Interfaces via perturbations in sensory stimuli. Similar to adversarial examples, these stimuli aim to exploit the limitations of the integrated brain-sensor-processing components of the BCI system.
  arXiv  Detail & Related papers  (2022-11-18T05:20:35Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography [51.45515911920534]
  A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector. A specific two branch convolutional neural network with larger kernels is designed as the feature extractor. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
  arXiv  Detail & Related papers  (2022-07-04T13:15:08Z)
• Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification [78.120927891455]
  State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines.
  arXiv  Detail & Related papers  (2021-12-05T21:57:22Z)
• EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks [68.01125081367428]
  Recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before.
  arXiv  Detail & Related papers  (2020-10-30T20:49:42Z)
• Emotion-robust EEG Classification for Motor Imagery [0.0]
  Developments in Brain Computer Interfaces (BCIs) are empowering those with severe physical afflictions through their use in assistive systems. Common methods of achieving this is via Motor Imagery (MI), which maps brain signals to code for certain commands. EEG is preferred for recording brain signal data on account of it being non-invasive. This work aims towards making MI-BCI systems resilient to such emotional perturbations.
  arXiv  Detail & Related papers  (2020-05-23T17:31:07Z)
• EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [65.32004302942218]
  Brain-Computer Interface (BCI) is a powerful communication tool between users and systems. Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
  arXiv  Detail & Related papers  (2020-01-28T10:36:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.