Accurate, Robust and Privacy-Preserving Brain-Computer Interface Decoding

• URL: http://arxiv.org/abs/2412.11390v1
• Date: Mon, 16 Dec 2024 02:37:38 GMT
• Title: Accurate, Robust and Privacy-Preserving Brain-Computer Interface Decoding
• Authors: Xiaoqing Chen, Tianwang Jia, Dongrui Wu,
• Abstract summary: EEG-based brain-computer interface (BCI) enables direct communication between the brain and external devices. EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs.
• Score: 15.550334083917935
• License:
• Abstract: An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While previous studies have addressed one or two of these issues, simultaneous accommodation of all three challenges remains challenging and unexplored. This paper fills this gap, by proposing an Augmented Robustness Ensemble (ARE) algorithm and integrating it into three privacy protection scenarios (centralized source-free transfer, federated source-free transfer, and source data perturbation), achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on three public EEG datasets demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness in all three privacy-preserving scenarios, even outperforming state-of-the-art transfer learning approaches that do not consider privacy protection at all. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• User Identity Protection in EEG-based Brain-Computer Interfaces [0.0]
  Brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. EEG is the most popular input signal in BCIs, due to its convenience and low cost. EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected.
  arXiv  Detail & Related papers  (2024-12-13T04:48:33Z)
• Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces [16.426546510800335]
  A brain-computer interface (BCI) enables direct communication between the brain and an external device. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs.
  arXiv  Detail & Related papers  (2024-12-10T06:42:46Z)
• Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces [17.24882553037956]
  A brain-computer interface (BCI) enables direct communication between the brain and an external device. EEG is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG signals inherently carry rich personal information, necessitating privacy protection.
  arXiv  Detail & Related papers  (2024-11-29T06:33:31Z)
• User-wise Perturbations for User Identity Protection in EEG-Based BCIs [18.96849505757419]
  We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.
  arXiv  Detail & Related papers  (2024-11-04T14:17:22Z)
• 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)
• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
  Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners. FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
  arXiv  Detail & Related papers  (2024-03-10T08:41:22Z)
• TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data [50.797729676285876]
  We propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously. We theoretically quantify the privacy guarantee through the lens of the emerging f-differential privacy (DP) and the Byzantine resilience of the proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-16T16:41:14Z)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• 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)

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.