Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network

Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeedback due to their non-invasive nature and ability to decode movement-related neural activity. Recent advances in deep learning, particularly convolutional neural networks, have improved the accuracy of motor imagery (MI) and motor execution (ME) classification. However, EEG-based BCIs remain vulnerable to adversarial attacks, in which small, imperceptible perturbations can alter classifier predictions, posing risks in safety-critical applications such as rehabilitation therapy and assistive device control. To address this issue, this study proposes a three-level Hierarchical Convolutional Neural Network (HCNN) designed to improve both classification performance and adversarial robustness. The framework decodes motor intention through a structured hierarchy: Level 1 distinguishes MI from ME, Level 2 differentiates unilateral and bilateral motor tasks, and Level 3 performs fine-grained movement classification. The model is evaluated on the publicly available BCI Competition IV-2a dataset, which contains multi-class MI EEG recordings from nine healthy subjects. Robustness is assessed under gradient-based adversarial attacks, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool, across varying perturbation strengths, with adversarial training incorporated during learning. Experimental results show that the proposed HCNN achieves a clean-data accuracy of 91.2% and exhibits reduced performance degradation under adversarial attacks compared with conventional CNN baselines. These results indicate that hierarchical architectures offer a viable approach for improving the reliability of EEG-based BCIs. All experiments were conducted exclusively on the BCI Competition IV-2a dataset using EEG data from healthy subjects.