CASTNet: Cycle-Consistent Attention-based Network for Decoding Open/Close Hand Movement Attempts using EEG

• Published in: Proceedings of ... International Joint Conference on Neural Networks pp. 1 - 10
• Main Authors: Ng, Han Wei, Thomas, Kavitha, Robinson, Neethu, Wai, Aung Aung Phyo, Liang, Leran Jenny, Khendry, Nishka, Nagarajan, Aarthy, Guan, Cuntai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30.06.2024
• Subjects: Adaptation models; Brain modeling; Deep learning; Feature extraction; Motors; Training; Transformers
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN60899.2024.10651226

Electroencephalogram based Brain-Computer Interface (EEG-BCI) employing motor Execution or imagery paradigms generally employs combination of multiple limbs namely right hand, left hand and foot to establish distinct control tasks. The generated neuronal patterns by multiple limb activity are distinct and have been offering promising impact on BCI-based rehabilitation and control applications for decades. However, a huge challenge for present BCI decoding systems lie in decoding finer actions of a single limb such as opening, closing, flexion and extension which is highly difficult for most present-day neural networks, on account of the highly overlapping brain-activations. The inherent high noise-to-signal ratio, inter-subject variability and intra-subject variability associated with the collected EEG signals makes the task challenging for even for deep learning networks which perform efficiently for multiple limb decoding. In this work, a novel network is introduced, named as Cycle-consistent Attention-based Spatio-Temporal Network (CASTNet), for the purposes of classifying open/close attempt based EEG signals of the right hand. The network uses spatio-temporal filters to extract distinguishable features associated with the motor attempts. The network further utilizes transformer layers to capture the long-term dependencies of EEG decoding network. Cycle-consistency is then applied towards the target data against the remaining training set in a subject-dependent setting. The network is validated upon a dataset comprising of 50 subjects performing open close hand movement attempts using right hand. The proposed network shows high efficacy in utilizing data in model adaptation scenarios.