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    Frequency domain channel-wise attack to CNN classifiers in motor imagery brain-computer interfaces
    (IEEE-INST electronics electrical engineers, 2024) Huang, Xiuyu; Choi, Kup-Sze; Liang, Shuang; Zhang, Yuanpeng; Zhang, Yingkui; Poon, Simon; Pedrycz, Witold
    Objective: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead. Methods: For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required. Results: Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks. Conclusion: Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected. Significance: To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.
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    Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Huang, Xiuyu; Liang, Shuang; Zhang, Yuanpeng; Zhou, Nan; Pedrycz, Witold; Choi, Kup-Sze
    For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.
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    Shallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classification
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Huang, Xiuyu; Choi, Kup-Sze; Zhou, Nan; Zhang, Yuanpeng; Chen, Badong; Pedrycz, Witold
    Electroencephalography (EEG) data across multiple individuals have a high variance. Directly using the data to train a deep learning (DL) model usually degrades the performance. To address this issue, we propose a shallow Inception domain adaptation framework to extract informative deep features from data of multiple subjects for accurate motor imagery (MI) recognition. To our best knowledge, the Inception architecture in DL is combined with a domain adaptation (DA) scheme for the first time for the MI classification task. The approach contains two compact Inception blocks that decode temporal features in different scales. In addition, we jointly optimize a novel combined loss function to reduce both marginal and class conditional discrepancies caused by the multimodal structure of EEG signals. The DA-based loss enables Inception blocks to take full advantage of their learning abilities to capture discriminative patterns of MI data from multiple subjects instead of relying on the target user only. To demonstrate the effectiveness of our approach, we conduct substantial experiments on two well-known data sets, brain-computer interface competition IV-2a and competition IV-2b. Results show that our model achieves better performance than state-of-the-art strategies. The proposed model is able to extract informative features from high-variant EEG data collected from different individuals and achieves accurate MI classifications.