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    Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Aslan, Sevgi Gokce; Yilmaz, Bulent
    The primary aim of this study was to assess the classification performance of deep learning models in distinguishing between resting state and motor imagery swallowing, utilizing various preprocessing and data visualization techniques applied to electroencephalography (EEG) data. In this study, we performed experiments using four distinct paradigms such as natural swallowing, induced saliva swallowing, induced water swallowing, and induced tongue protrusion on 30 right-handed individuals (aged 18 to 56). We utilized a 16-channel wearable EEG headset. We thoroughly investigated the impact of different preprocessing methods (Independent Component Analysis, Empirical Mode Decomposition, bandpass filtering) and visualization techniques (spectrograms, scalograms) on the classification performance of multichannel EEG signals. Additionally, we explored the utilization and potential contributions of deep learning models, particularly Convolutional Neural Networks (CNNs), in EEG-based classification processes. The novelty of this study lies in its comprehensive examination of the potential of deep learning models, specifically in distinguishing between resting state and motor imagery swallowing processes, using a diverse combination of EEG signal preprocessing and visualization techniques. The results showed that it was possible to distinguish the resting state from the imagination of swallowing with 89.8% accuracy, especially using continuous wavelet transform (CWT) based scalograms. The findings of this study may provide significant contributions to the development of effective methods for the rehabilitation and treatment of swallowing difficulties based on motor imagery-based brain computer interfaces.
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    EEG-based decoding of swallowing intention using a transformer-enhanced deep learning approach
    (Elsevier Sci Ltd, 2026) Aslan, Sevgi Gokce; Yilmaz, Bulent
    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.
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    Examining Tongue Movement Intentions in EEG with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation
    (Ieee, 2024) Aslan, Sevgi Gokce; Yilmaz, Bulent
    Dysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, Kernel, and Neural Network were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN and SVM, exhibited lower accuracy rates compared to ensemble methods like AdaBoost and Random Forest, which are effective in handling complex datasets. Additionally, a deep learning approach achieved an accuracy rate of 83%. Overall, this study demonstrates the promising role of BCI technologies and machine learning techniques in dysphagia rehabilitation.
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    Examining Tongue Movement Intentions in EEG-Based BCI with Machine and Deep Learning: An Approach for Dysphagia Rehabilitation
    (Sciendo, 2024) Aslan, Sevgi Gokce; Yilmaz, Bulent
    Dysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, and Kernel were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN (79.4%) and SVM (63.4%) exhibited lower accuracy rates compared to ensemble methods like AdaBoost, Bagging, and Random Forest, all achieving high accuracy rates of 99.8%. These ensemble techniques proved to be highly effective in handling complex EEG datasets, particularly in distinguishing between rest and imagination phases. Furthermore, the deep learning approach, utilizing CNN and Continuous Wavelet Transform (CWT), achieved an accuracy of 83%, highlighting its potential in analyzing motor imagery data. Overall, this study demonstrates the promising role of BCI technologies and advanced machine learning techniques, especially ensemble and deep learning methods, in improving outcomes for dysphagia rehabilitation.
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    Multi-scale spatial attention network for rest and imagination classification in saliva and water swallowing paradigms
    (Elsevier Sci Ltd, 2026) Aslan, Sevgi Gokce; Yilmaz, Bulent
    In this study, a multi-scale spatial attention network (MS-SAN) architecture is proposed to distinguish motor imagery and rest paradigms in electroencephalogram (EEG)-based swallowing analyses. Motor imagery is a mental process associated with motor control and cognitive processes, and EEG is a powerful tool that offers high temporal resolution and accuracy in monitoring these processes. The study investigates the MS-SAN model, which can effectively learn frequency bands in EEG signals and model inter-individual variations. The study employed two distinct experimental paradigms to investigate the processes involved in saliva swallowing and water-induced swallowing. The first paradigm focused on examining the cognitive and motor mechanisms underlying saliva swallowing, while the second paradigm extended this investigation to include the effects of water intake on the swallowing process. This dual approach provided valuable insights into how external factors, such as the presence of water, influence the dynamics of swallowing behavior. CSP (Common Spatial Pattern) filtration was applied to extract spatial patterns from EEG data, and the performance of motor imagery and rest paradigms in different frequency bands (delta, theta, alpha, beta, gamma) were evaluated. For each frequency band, motor imagery and rest states were transformed into topographical representations using spatio-spectral analysis. Statistical analysis, including the Friedman test and post-hoc pairwise comparisons using the Dunn-Sidak test, was conducted to evaluate the differences among EEG frequency bands. The study shows that the MS-SAN model exhibits high classification performance, especially in low-frequency bands (delta and theta), and accurately distinguishes neurophysiological differences between motor imagery and rest states. The results show that the MS-SAN model provides an effective classification method for EEG-based swallowing analysis. These techniques hold great potential for clinical applications, motor rehabilitation, and neurophysiological analysis.