## 4 Discussion
We acknowledge that the CNN-LSTM model developed in this study achieved slightly lower accuracy compared to our previous investigation involving 19 acute and subacute stroke patients, where linear discriminant analysis (LDA) and support vector machine (SVM) classifiers yielded accuracy rates ranging from 89.75% to 90.37% for seven gesture classes [13]. However, several important considerations support the continued value of DNNs and the bilateral data fusion approach. In conventional models, EMG feature engineering for gesture classification in paretic muscles requires substantial manual effort, including the extraction of optimal feature vectors such as wavelet packet coefficients [14]. These feature sets often lack generalizability, particularly in post-stroke populations, due to high inter-individual variability, thus limiting their practical application. In contrast, the CNN-LSTM architecture used in this study (see Figure 5) offers a balanced approach, capturing temporal signal dynamics while maintaining computational efficiency [8; 10]. By automatically learning relevant representations from filtered four-channel EMG data, this model provides a more robust and adaptable solution than traditional methods, better suited to the heterogeneity of stroke-related motor impairments [3; 14]. For illustration, this explains why models that exclude the rest gesture do not exhibit a significant drop in ROC curve performance (see Figures 7m,n and 8m,n) or in other evaluation metrics (see Tables 2 and 3). The resting position, being a static gesture, typically yields a low signal-to-noise ratio and minimal muscle activation [27]. Traditional supervised learning approaches, such as SVM, often depend on magnitude-based features (e.g., temporal moments), which are highly susceptible to noise and approximation errors. In contrast, CNN-LSTMs learn more robust feature representations directly from raw or filtered EMG signals, making them less prone to classification loss [13; 21; 22; 26]. This characteristic may account for the consistently high SP scores observed across most models. The lower SP value and higher SD observed only in the two-gesture subset can likely be attributed to the limited amount of training data and restricted number of gesture classes, which constrain the model's predictive capability. Regarding the classification performance of the three-gesture subset (3G-A) in dataset A (rest, wrist extension, and thumbs up) and the four-gesture subset (4G-B) in dataset B (rest, wrist flexion, wrist extension, and hand fist), it should be noted that the development of co-contraction in stroke patients often leads to abnormal extensor movements and increased muscle tone [4; 32]. These neuromuscular alterations may introduce signal overlaps between gesture classes, thereby affecting classification accuracy [12; 24]. Specifically, EMG signals, in terms of voltage and intensity, may become more similar due to the abnormal coupling of flexor and extensor muscle groups. This condition typically results in heightened flexor tone and creates an imbalance in extensor activity, disrupting the normal timing and coordination of agonist-antagonist muscle pairs [32; 33]. As a result, signal intensity may decline, and different gestures may become less distinguishable (see Figure 3). These pathophysiological patterns likely contribute to the lower cross-validated AUC-ROC values observed for the fist and thumbs up gestures, particularly in comparison to the rest state (see Figure 7k,l and Figure 8k,l). Consequently, we suggest that such post-stroke motor abnormalities reduce gesture separability, thereby decreasing the predictive reliability of DL models and resulting in lower F1-scores when classifying hemipleggic EMG data.