## 4 Discussion
Overall, the selected gesture subsets (as shown in Tables 2 and 3) are well-distributed to capture distinct muscle coactivation patterns and bursts across a range of upper limb muscle groups (see Figures 7 and 8), spanning fine motor gestures like thumbs up and index pinch to gross motor movements such as wrist flexion [15]. This systematic approach enables a gradual, detailed evaluation of DNN's predictive performance, particularly in the context of EMG pattern recognition on the hemiplegic side [10; 19]. To sum up, based on the analysis of two datasets and the observed impact of bilateral data fusion in a cohort of 25 stroke patients, we hypothesize that the CNN-LSTM architecture, by combining automated feature extraction through CNNs with the temporal sequence modeling capabilities of LSTMs, holds significant potential for EMG-based gesture recognition. This strength lies in the model's capacity to generalize across heterogeneous datasets during training, enhancing its adaptability to complex neuromuscular patterns in post-stroke populations [21; 22]. We propose that incorporating data augmentation techniques during the training phase, such as introducing EMG data from healthy individuals or participants without neurological deficits, could effectively simulate the benefits of bilateral data fusion (see Figures 4 and 6). This strategy may enhance the model's ability to predict paretic gestures, even in the acute and subacute stages of stroke recovery, despite the atypical EMG patterns and movement clusters that deviate from normal function which are typically seen during the post-stroke period [13; 32; 33]. For example, openly accessible datasets such as EMG-EPN-612 or NinaPro may be utilized to merge additional myoelectrical data [2; 9; 11; 17; 18; 20; 22; 23; 25]. These datasets are important for testing and validating this hypothesis in future research. Regarding study limitations, the sample size of 25 stroke patients may be insufficient to achieve consistently high F1-scores and accuracy metrics (see Tables 2 and 3). While this limitation was partially mitigated by incorporating data from the non-paretic side during training, it remains a relevant constraint. In terms of deployment strategies, utilizing the current software and hardware presented additional challenges. The training time for a single CNN-LSTM model, depending on the number of gestures used and data augmentation with unaffected gestures, ranged from 4 to 12 min, while model evaluation required 120 to 180 min per random split. Moreover, employing 10-fold cross-validation iterated 100 times significantly increased the evaluation time, particularly due to the high number of iterations. This method may be necessary for different stroke populations, as the DNN network requires manual tuning to accommodate variability in patient characteristics. In addition, the network's parameter configuration was determined empirically and proved highly sensitive. Previous studies have shown that conventional supervised learning classifiers, when combined with feature extraction from well-defined domains, such as data from the non-paretic side, can achieve strong performance [12; 13; 15]. However, when analyzing EMG data from the hemiplegic side, as demonstrated in our earlier work, predefined feature sets often fail to generalize due to inter-patient variability. In such cases, brute-force or semi-brute-force selection methods are more effective. Therefore, we argue that the application of DNNs is justified, as they provide a more flexible and robust approach in terms of the myoelectrical decoding of hand movements of stroke patients [14]. We believe that the combination of bilateral data fusion and CNN-LSTM-based EMG decoding holds considerable potential for practical applications, particularly in scenarios involving severe paresis or complete paralysis.