## 3 Results  
### Comparison of the Pooling Functions  
Four pooling functions are tested. The performance of the linear softmax pooling function in EONND was very unstable, and the effective threshold selection range was narrow. This meant that the parameter was difficult to tune, so it was excluded from the comparison. In addition, since the F1 score reflects the overall performance of the algorithms, this section takes the F1 score as an example to show the performance trends of different pooling functions in the threshold range of 0.5 to 0.6 for processing different SNR values. Additional details on the performance evaluation can be found in the Supplementary Materials. In particular, data (onset bias, offset bias, precision, recall, and F1 score) are reported separately, and bar diagrams comparing the three pooling functions are also provided.  
Figure 6a-c shows the 3D surface plots of F1 score for the three pooling functions with the SNR on the X-axis, the threshold values on the Y-axis, and the F1 score statistics for the detector at the corresponding parameter settings on the Z-axis. Figure 6d shows that the histogram of the detection performance of the three pooling functions varied with SNR under the optimal threshold conditions (max T = 0.53, exp. T = 0.54, ave. T = 0.54). It can be seen that the three pooling functions showed a consistent trend with condition changes, and the exp. and ave. pooling functions had a similar performance, while max pooling was stronger. When EONND obtained the optimal threshold parameter, as shown in Figure 6d, although the peak performance of the three pooling functions was very close at a higher SNR, only with the max pooling function did EONND still maintain a higher F1 score as SNR decreased, indicating that the use of the max pooling function in the EONND framework allows the detector to be more robust to noise.  
The same conclusion was drawn from the results tested on real EMG signals. As shown in Figure 7, the performance of EONND with the three pooling functions on the real EMG signals was still the best with the max pooling function. EONND with the max pooling function achieved all three reliability metrics at higher values than the other pooling functions, with smaller deviations.  
In terms of detection accuracy, max pooling is still the best choice. Table 1 shows the average values of the detection bias of the three pooling functions for the onset and offset under the optimal threshold parameters. Three phenomena can be observed from Table 1: (1) The exp. and ave. pooling functions had similar accuracy, while max pooling showed a better performance; (2) For the simulated signals, the detection of the offset points was more accurate than the onset points; (3) For real EMG signals, the accuracy of the onset points was higher than the offset points.  
In summary, by testing four pooling functions, max pooling should be used as the best matching choice for EONND. The following section will compare the performance of EONND with the max pooling function against several popular EMG activity detectors to highlight the effectiveness of the proposed method in this paper.  
Figure 6: Comparison of the effect of max, exp., and average pooling functions on the EONND framework at different SNRs.  
Figure 7: Performance comparison of the three pooling functions on real EMG signals.