Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces

• URL: http://arxiv.org/abs/2212.00727v1
• Date: Mon, 28 Nov 2022 11:05:32 GMT
• Title: Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces
• Authors: Xiaoqing Chen and Dongrui Wu
• Abstract summary: 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.
• Score: 28.686844131216287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but few had considered its security. Recent studies, however, have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detection of adversarial examples is crucial to both the understanding of this phenomenon and the defense. This paper, for the first time, explores adversarial detection in EEG-based BCIs. Experiments on two EEG datasets using three convolutional neural networks were performed to verify the performances of multiple detection approaches. We showed that both white-box and black-box attacks can be detected, and the former are easier to detect.

Related papers

• Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs [20.239554619810935]
  ABAT performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies. adversarial training further robustifies the classification boundary.
  arXiv  Detail & Related papers  (2024-11-04T13:56:54Z)
• 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)
• A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition [14.199298112101802]
  This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SICLEER) We propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss.
  arXiv  Detail & Related papers  (2024-05-12T11:51:00Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• 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)
• 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)
• 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)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• 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.