## 4 Discussion  
Based on our previous analyses, EONND showed better reliability and accuracy than other detectors on simulated and real EMG. According to the results, five additional issues need to be discussed in detail. 1. The reason for the good robustness of EONND. Although the detector proposed in this paper was not specifically designed with an adaptive mechanism, the special neural network structure of EONND can indirectly give EONND some adaptiveness by pooling multiple energy operator features from the output of the \"convolutional\" layer (this is not a real convolutional layer but shows the position relationship). Therefore, EONND with the max pooling function still maintained a high change-point-detection performance for signals with very low SNR. In addition, the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \ \begin{tabular}{c} **SNR** \\ **(dB)** \\ \end{tabular}  & **EONND** & **abs\_TKEO** & **abs\_MTEO** & **AMP** & **Biosppy** & **ENV** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **  
baseline segments of low SNR signals contain a lot of noise, which seriously affects the accuracy of the traditional threshold-based methods to determine the threshold values by obtaining the baseline segment features. EONND does not require any prior knowledge of the signal for change-point detection and is not disturbed by noise in the baseline segments. This is because EONND is essentially equivalent to a trained neural network. Therefore, EONND has a strong advantage in the processing of low SNR signals. The issue of setting EONND hyperparameters. In the EONND method proposed in this paper, the parameters of the \"convolution\" layer were fixed by a multi-resolution energy operator; therefore, updating the parameters through training was not required. However, in practice, EONND still has two hyperparameters that need to be set or tuned in advance; the energy operator scale factor \(k\) and the threshold T. The scale factor \(k\) of the energy operators determines how many sampling points can be observed by EONND at a time. Since this is a preliminary exploration, this paper did not discuss the issue of how to obtain the optimal value of \(k\). Instead, we directly set the value recommended by relevant studies, i.e., \(k=15\), because such a setting can cover the frequency of EMG signals [47; 48]. For the problem of setting the threshold T, we used the traversal method to predict the value. The traversal method requires a certain amount of data with labels, but not as much data as required by the neural network algorithms. As the SNR of the simulated signal decreased, the detection performance of EONND did not show significant degradation, indicating that EONND also has good performance under a fixed threshold value. It should be noted that the optimal threshold values for real and simulated signals are different (T\({}_{\text{real}}=0.506\), T\({}_{\text{simulated}}=0.53\)). Therefore, the development of an automatic mapping mechanism between the threshold and signal will be the focus of future research. The problem of abnormal accuracy performance statistics. When the detection accuracy metrics of the simulated EMG were counted, an abnormal scenario with bias values of 0 occurred, indicating that the detection values did not deviate from the ground truth. It is important to note that this result only represents a performance trend, as such accuracy is almost impossible to achieve when dealing with real-world signals. In addition, all detectors had a higher detection accuracy for the offset points than for the onset points, but according to previous studies [62] and the detection of real EMG signals in this paper, the normal phenomenon for the detection of real EMG signals should be higher accuracy for onset than for offset, which is determined by MUAPs discharge characteristics. However, the simulated signal simplified the signal model and therefore produced the opposite result.