## 3 Results
### Detection
#### 3.1.1 Laboratory Setting--Dataset  
Figure 7 shows the results achieved for each experimental session, training the model on the control data and classifying the four different configurations (Control Data, _Test same Chs diff Ref, Test same Ref diff Chs_, and _Test diff Ref diff Chs_). The four curves represent the four conditions on which the analysis was performed. Specifically, the blue curve identifies the classification results in the control condition used to train the classification model. The graph shows that except for a few experimental sessions, it is quite evident that the configuration \"_Test diff Ref diff Chs_\", which inherently represents a greater shift in the data compared to the control configuration, seems to achieve very low accuracy values different from those achieved by the control configuration. This difference is also noticeable in many experimental sessions for the other two configurations, which are also affected by data shifts. Figure 8 represents the average accuracy curve over 52 sessions (i.e., all available sessions considering the nine subjects described above) on each configuration and the corresponding Wilcoxon test performed, comparing the values achieved in the control condition with those achieved in the remaining 3 test configurations. It is quite evident how the _\"Test diff Ref diff Chs\"_ configuration, even when averaging all the values from the 52 experimental sessions, does not achieve good levels of classification accuracy compared to what one would obtain by training and using the classification model with the control configuration. The other configurations also differ significantly from the values achieved by the control configuration. The Wilcoxon test shows that these differences are significant (\(p<0.05\)) for all three configurations (the three dashed curves appear to overlap, and therefore only one seems to be visible). Comparing the average accuracy values achieved by the three test configurations with respect to the control configuration, using the Cohen test, we note that the difference in the means is very significant, having for all three comparisons a D value greater than 2 (in particular 9.11 for Control vs. _Test diff Ref diff Chs_; 3.43 for Control vs. _Test same Ref diff Chs_; 2.94 for Control vs. _Test same Chs diff Ref_). Figure 7: Accuracy was achieved in classifying four configurations per experimental session. Each line displays model training accuracy on control data and classification of the four configurations. The Y-axis ranges from 50 to 100; if a configuration’s curve is not visible, its accuracy is below 50. Numbers 1 to 6 identify the registration sessions present for each participant. Table 2 displays classification accuracy values and corresponding \(p\)-values achieved at specific levels of decimation (in seconds) across different channel and reference configurations. The \(p\)-value is computed between the control configuration and each of the test configurations; hence, the value is not present in the column related to the control data. Figure 9 shows the percentage of anomalies (%CS) for each experimental configuration at different values of the _contamination_ parameter, split per difficulty level (Easy--Hard). Each point on the curve represents the percentage of records within the subset (i.e., the value on the X-axis) identified as having a shift in the data compared to control data (i.e., each point of the curves is the average value across all experimental sessions). The Wilcoxon test showed that all the performance levels for each condition differed significantly from the control one (\(p<0.05\)). Furthermore, it can be observed how the data from the 'Hard' condition seem to be more inclined to be identified as shifts, as they have a higher number of records identified as shifts, even with the same percentage of anomaly and configuration. Additionally, as seen in the classification performance, in this case, the worst configuration appears to be the _\"Test diff Ref diff Chs\"_ configuration.