## 4 Discussion  
The goal of this research was to investigate the possibility of downscaling the number of sensors necessary to decode imagined movements from EEG signals. To determine whether reducing the number of sensors used for this purpose is feasible and to identify their optimal locations, an experimental study was performed. This study consisted of training a well-known decoding pipeline using different sensor subsets and comparing the cross-validation results. The data used in this study were acquired from 15 participants performing MI in our lab. To ensure that the data are qualitative and contain motor-related activity, the signals from the executed movement were used as ground truth and compared with the imagined movement signals in a VEP analysis, both with and without feedback. The expected VEP peaks [29] could clearly be identified from the visualization of the signals in Figure 4. A delay of around 50 ms was observed compared to the expected timings, which could be attributed to the reaction speed of the individuals or due to syncing delays introduced by the LSL software (v 1.15.0). This confirms that the data are indeed qualitative and contain the expected movement-related information. This indicates that models should learn from the motor-related brain activity and not from some unrelated patterns that co-occur with imagined movement initiation. The obtained cross-validation results are in line with literature that uses similar models [53, 54]. However, it is important to note that the number of classes used for classification and hyperparameter choices can greatly influence decoding performance. For example, using user-specific filter frequencies can improve results [55]. Additionally, the evaluation method plays an important role, resulting in stronger or weaker conclusions regarding performance, depending on the method employed. A cross-validated result cannot be compared to test accuracy with a single train-test split [56]. From the cross-validation results on imagined movement data from both feedback and non-feedback runs, we observe that the decoding accuracy does not significantly differ between the chosen sensor subsets. This outlines the feasibility of downscaling the number of sensors for BCI decoding purposes. It is noteworthy to mention that a large inter-individual variability exists, which is in line with current literature [57]. The \(p\)-values for the smaller sensor subsets are lower, which seems to indicate that there is a threshold for the minimal number of necessary sensors to maintain decoding performance. This threshold is yet to be determined for MI decoding. When comparing decoding performance on data from individual participants, the variance in decoding accuracy is reduced for different sensor subsets, suggesting a more stable decoding performance. However, a large intra-individual variability remains, which makes it harder to train a model using all sessions for a specific user. Finding methods to reduce this variability is an important part of BCI research [58, 59]. We also observe that there is a  
Figure 7: Cross-validation accuracies for different sensor subsets for the participant with the best mean accuracy (P12) (**a**) and lowest accuracy (P02) (**b**), respectively, when using the full sensor set. difference in which specific subsets of sensors seem to be optimal for different individuals. This result is also consistent with literature that aims to identify optimal user-specific sensor subsets [21]. This finding indicates that both defining a general set of sensor locations and performing a user-specific search for the optimal subset within this set are important steps toward real-life BCI applications. Only one participant used kinesthetic MI for all movements, while two used both visual and kinesthetic MI depending on the movement. The other participants used visual MI, with three using an internal perspective, seven using an external perspective, and the remaining two participants choosing both internal and external perspectives depending on the movement.