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SSVEP-based brain-computer interfaces are vulnerable to square wave attacks

  • Research Paper
  • Special Focus on Brain Machine Interfaces and Applications
  • Published:
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Abstract

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) have attracted wide attention in recent years. Steady-state visual evoked potential (SSVEP) is one of the most popular BCI paradigms, which has high information transmission rate and short user calibration time. Recent research has shown that EEG-based BCIs are under the threat of adversarial examples. However, existing attack approaches are very difficult to implement in a real-world system. Considering SSVEP’s dependency on the frequency information, this paper proposes to use square wave signals as adversarial perturbations to attack SSVEP-based BCIs, which are easy to generate and apply in practice. EEG trials contaminated by the square wave perturbation can be classified into any target class specified by the attacker. Compared with previous approaches, our perturbation can be implemented much more easily. Experiments on two SSVEP datasets demonstrated the efficiency and robustness of the proposed approach in attacking two SSVEP classifiers based on canonical correlation analysis, exposing a critical security problem in SSVEP-based BCIs.

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Acknowledgements

This work was supported by Open Research Projects of Zhejiang Lab (Grant No. 2021KE0AB04) and Technology Innovation Project of Hubei Province of China (Grant No. 2019AEA171).

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Authors and Affiliations

  1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Rui Bian, Lubin Meng & Dongrui Wu

  2. Zhejiang Lab, Hangzhou, 311121, China

    Dongrui Wu

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  1. Rui Bian
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  2. Lubin Meng
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  3. Dongrui Wu
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Correspondence to Dongrui Wu.

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Bian, R., Meng, L. & Wu, D. SSVEP-based brain-computer interfaces are vulnerable to square wave attacks. Sci. China Inf. Sci. 65, 140406 (2022). https://doi.org/10.1007/s11432-022-3440-5

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  • DOI: https://doi.org/10.1007/s11432-022-3440-5

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