Aslan, Sevgi GokceYilmaz, Bulent2026-04-042026-04-0420261746-80941746-8108https://doi.org/10.1016/j.bspc.2026.109861https://hdl.handle.net/11616/109688Swallowing is a vital motor function that ensures safe and efficient nutrient intake, yet it remains largely underrepresented in brain-computer interface (BCI) research, particularly in the domain of motor imagery (MI). In this study, we propose a novel deep learning framework that combines Convolutional Neural Networks (CNNs) with a transformer-based architecture to classify EEG signals corresponding to swallowing MI and resting states. The model leverages CNNs to extract local temporal features and applies self-attention mechanisms to capture long-range dependencies across EEG time series, enhancing both performance and interpretability. EEG data were collected from 30 healthy participants (aged 18-56) using a 16-channel wearable EEG cap at a 500 Hz sampling rate. Participants alternated between swallowing MI tasks and rest conditions. The classification performance of the proposed model was evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach to ensure subject independence. Our results demonstrate that the transformer-based model achieved the highest average classification accuracy (80.11% +/- 9.88), precision (81.11% +/- 10.65), and recall (80.67% +/- 16.82), outperforming baseline models such as EEGNet, ShallowConvNet, and DeepConvNet. Statistical comparisons using the Friedman test with Bonferroni correction confirmed the model's significant superiority (p < 0.005). Additionally, t-SNE visualizations revealed distinct clustering between swallowing MI and rest conditions, suggesting robust discriminative feature learning. These findings underscore the potential of attention-based architectures in decoding complex oropharyngeal motor imagery from EEG. The proposed framework offers a promising foundation for future BCI-driven neurorehabilitation applications targeting swallowing disorders such as dysphagia.eninfo:eu-repo/semantics/closedAccessEEGSwallowingMotor ImageryBCITransformerDeep LearningDysphagiaArticle11910.1016/j.bspc.2026.1098612-s2.0-105030501798Q1WOS:001697087500003Q2