Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similar activation patterns between tongue movements and swallowing and

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... ss movements artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of dual-tree complex wavelet transform to build multiple training models to detect MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded an averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton based on 10 healthy subjects, which were significantly better than existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for 1 stroke patient, demonstrating the detectability of MI-SW and MI-Ton from idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for 10 healthy subjects and 1 stroke patient using MI-Ton model. These results and the subject-wise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.