## 5 Conclusions  
This study presents the first application of a CNN-LSTM-based model for surface EMG signal classification of hand gestures in subacute stroke patients. By introducing a novel bilateral data fusion approach, incorporating non-paretic hand gesture data from the same individuals into the training process, we addressed a major limitation in stroke-specific EMG data availability. Using an all-in-one model with seven gestures, the highest mean accuracy achieved was 83.17%. Furthermore, we identified an optimal subset of gestures yielding a mean accuracy of 88.36%, along with other improved performance scores. The model was evaluated through cross-validation using EMG data from 25 stroke patients (mean time since onset: 16.0 \(\pm\) 8.6 days for dataset A and 19.2 \(\pm\) 13.7 days for dataset B; average FMA-UE score: 37 \(\pm\) 20). Our results demonstrate that bilateral data fusion significantly enhances the decoding of myoelectrical signals, with the most notable improvement observed in the three-gesture subset (rest, extension, and thumbs up), where accuracy increased by 7.7%. Furthermore, the F1-score, a key metric for evaluating classification performance in imbalanced datasets, showed statistically significant improvements in most scenarios when comparing paretic-only data to fused data. Although the overall accuracy may be lower compared to some canonical supervised learning algorithms, the proposed DL framework provides a compelling advantage by eliminating the need for manual feature engineering, simplifying model development, and more effectively accommodating the complex, lesion-specific nature of stroke EMG data. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s25123664/s25123664/s1](https://www.mdpi.com/article/10.3390/s25123664/s25123664/s1)]([https://www.mdpi.com/article/10.3390/s25123664/s25123664/s1](https://www.mdpi.com/article/10.3390/s25123664/s25123664/s1)), Table S1. Gesture-specific performance of a CNN-LSTM model on stroke EMG dataset A; Table S2. Gesture-specific performance of a CNN-LSTM model on stroke EMG dataset B.  
Conceptualization, A.A. and H.K. ; methodology, A.A. and H.K. ; software, H.K. and A.A.; validation, H.K. and A.A.; formal analysis, A.A. and H.K. ; investigation, A.A., H.K., A.M. ([PERSON]), H.W. and A.Z. ; resources, H.K. and A.M. ([PERSON]); data curation, A.A., H.K., A.M. ([PERSON]), H.W. and A.Z. ; writing--original draft preparation, A.A. and H.K. ; writing--review and editing, A.A., H.K., A.M. ([PERSON]), H.W., A.Z., S.W., A.M. ([PERSON]), K.S., H.N. and E.I. ; visualization, H.K. and A.A.; supervision, H.K., A.M. ([PERSON]), K.S. and E.I. ; project administration, H.K., A.M. ([PERSON]), A.Z., S.W., A.M. ([PERSON]), K.S., Y.M. and E.I. ; funding acquisition, H.K. and A.M. ([PERSON]), K.S., H.N. and Y.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research conducted at the University of Tsukuba Hospital, received support from the Japanese Ministry of Education, Culture, Sports, Science, and Technology through a Grant-in-Aid for Scientific Research (B) JSPS KAKENHI [23K25244] and funded by AMED \"Seeds A\" grant [HLI05019] issued by the University of Tsukuba. Finally, we thank the Center for Cyber Medicine Research, based at the University of Tsukuba, for the financial support that made this publication possible. **Institutional Review Board Statement:** Written informed consent was secured from all participants involved in our study. The study protocols, specifically R02-204 and R04-041, received approval from the Ethics Committee of the University of Tsukuba Hospital in Tsukuba, Japan. This study is in alignment with the Declaration of Helsinki. The clinical research is registered with the Japan Registry of Clinical Trials, identified by the registration number jRCT1030220380. **Informed Consent Statement:** Each patient in this study provided written informed consent, encompassing permission for publication.