## 4 Discussion  
As previously described, we split the stroke data into datasets A and B to assess result consistency across different post-stroke time periods independently. This approach allowed us to explore more precisely the conditions under which CNN-LSTM-based classification is most effective. In doing so, we improved the accuracy and reliability of our findings and strengthened the basis for our interpretations and conclusions. To the best of our knowledge, this study is the first to apply a hybrid CNN-LSTM architecture for decoding volitional hand gestures in subacute stroke patients [2, 8, 10]. Notably, efficient DL-based classification was achieved with only a limited number of surface EMG sensors on the forearm and hand, sampled at 1 kHz. To continue, bilateral data fusion for hemiplegic hand gesture recognition, using non-paretic extremity training data, significantly enhanced CNN-LSTM model performance. This improvement is clearly demonstrated in Figures 7 and 8, with the most pronounced performance gain observed in the three-gesture subset, where classification accuracy increased from 73.01% to 78.42% in dataset A (see Table 2) and from 77.95% to 85.69% in dataset B (see Table 3). A visual overview of the method and detailed cross-validation procedures are presented in Figures 4 and 6, respectively. We believe this approach not only improves classification performance but also expands the practical applicability of DNNs in neurorehabilitation. It is worth mentioning that our EMG classification models, both with and without the proposed bilateral data fusion approach, achieved satisfactory recognition of paretic hand movements across various gesture sub-models in both datasets. The performance levels observed are comparable to those reported in prior CNN-LSTM-based classification studies [22, 23, 24, 26]. As this is the first study to apply DL to subacute myoelectric gesture recognition, direct comparisons with similar post-stroke studies are limited [8, 17]. However, parallels can be drawn with related investigations involving chronic stroke survivors, patients with post-traumatic brain injury hemiplegia, amputees, and healthy individuals whose EMG signatures were used in CNN-LSTM models [8, 22]. For example, [PERSON] et al. evaluated eight patients in the chronic stage of stroke (1 to 13 years post-onset) using a CNN-LSTM model to control a robotic arm across six hand gestures, achieving an accuracy of 72.81% via intra- and inter-subject transfer learning [10]. In another study, [PERSON] et al. examined a single subject with severe brain trauma using a similar network [26]. Their results highlighted the challenge of generalizing non-paretic gesture recognition to the hemiplegic side, with recognition accuracy as low as 10.67% for the affected limb, compared to 56.00% for the unaffected side. These findings underscore the inherent complexity of paretic gesture recognition across diverse neurological conditions. Moreover, it is important to emphasize that the EMG signals from the non-paretic side, used in the bilateral fusion approach, should not be considered equivalent to those from healthy individuals. In our study, these signals were collected from the non-paretic upper limbs of subacute stroke patients, who presented with neurological abnormalities such as tremors, ataxia, or uncoordinated motor control [3, 13]. On top of that, such patientsoften face significant mental and physical burdens, including muscle weakness and fatigue, which can limit their ability to perform even basic hand movements with precision [4; 5]. However, despite these limitations, our findings, as well as evidence from prior research, suggest that the principle of muscle coactivation remains applicable, since the anatomical structure of the upper limb remains intact [32]. This suggests that EMG signal patterns from the non-paretic side have physiological similarities to those of the paretic side, thus possessing valuable insights for neurorehabilitation strategies [17].