EEG-based decoding of swallowing intention using a transformer-enhanced deep learning approach
Küçük Resim Yok
Tarih
2026
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Swallowing 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.
Açıklama
Anahtar Kelimeler
EEG, Swallowing, Motor Imagery, BCI, Transformer, Deep Learning, Dysphagia
Kaynak
Biomedical Signal Processing and Control
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
119
