## 4 Measurement Results
### Baseline Model  
With a total number of 2772 trials (11 participants x nine series x 28 trials) and three color channels (RGB), the number of grayscale scalograms produced per event is 8316. Since one classification involves a pair of two events, the total number of images is 16,632. After flattening the separate grayscale scalograms, the total number of RGB images per pair of classifications is 5544. This feature extraction process can take up to 3-4 h for a single classification. The 5544 scalograms are split into training and validation sets in an 80:20 ratio, as mentioned before. Using conventional equations, the five evaluation scores for the classifications are obtained. Intersubject classification is conducted between two unique pairs of events, resulting in six pairs for classification across all 11 participants. Table 4 indicates that the baseline system (average values) achieves a range of accuracy from 80.87% to 99.73%, with nearly perfect accuracy. Precision, sensitivity, and specificity range from approximately 71% to 100%, with the lower end of the range predominantly observed in the first three pairs. The average accuracy, precision, sensitivity, and specificity across all six pairs are 90.33%, 90.06%, 91.11%, and 89.53%, respectively. The average F1 score is 0.90. From Figure 22a, it is evident that the performance metrics for the first three groups are about 10-30% lower than those for the last three groups. The F1 score follows a similar trend between these two sets of groups (Figure 22b). This performance drop in the  
Figure 21: (**a**) Ten-fold cross-validation accuracies for the forward and reverse pair and the (**b**) grasp and release pair. (**c**) Confusion matrices showing 56.52% accuracy and (**d**) 51.89%. first three groups could be attributed to the forward hand movement (FW) event, as these groups all involve the FW event. Enhancing accuracy might be possible by adjusting the FW slices to a better temporal location. Refer to the intrasubject classification for another potential cause of this performance drop. The training and validation progress plots of the classifier for FW-RV and GP-RL. For intrasubject classification, the pair with the lowest accuracy from intersubject classification (FW-GP) is selected. The data for this pair are separated by individual participants, and classification is performed on these individual groups. As expected, both accuracy and F1 score improved for all participants in intrasubject classification, except for participant 3 (see Figure 23a,b). The low accuracy score for intersubject classification might be due to participant 3's forward hand movement data, although further research is needed to confirm this. For some participants, the baseline method achieves perfect scores for all performance metrics, as shown in Table 5. The average accuracy, precision, sensitivity, and specificity across all 11 participants are 91.36%, 92.01%, 88%, and 94.73%, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Pair** & **Accuracy (\%)** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score** \\ \hline FW-GP & 80.87 & 75.99 & 90.25 & 71.48 & 0.8251 \\ \hline FW-RL & 82.31 & 87.29 & 75.63 & 88.99 & 0.8104 \\ \hline FW-RV & 84.39 & 84.83 & 83.75 & 85.02 & 0.8429 \\ \hline GP-RL & 98.56 & 99.45 & 97.56 & 99.46 & 0.9854 \\ \hline GP-RV & 99.73 & 100 & 99.46 & 100 & 0.9973 \\ \hline RL-RV & 96.12 & 92.8 & 100 & 92.24 & 0.9626 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Intersubject Classification Results (Average Values). Figure 22: (**a**) Scores of 4 performance metrics for 6 pairs of interest. (**b**) F1 score of the 6 events. The training and validation progress plots of the classifier for participant 2 and participant 5. A comparison of performance between the baseline system and that of the selected paper. Since the classification in paper [17] is performed on events vs. non-events, a direct comparison cannot be made.