## 4 Discussion
It would be of interest to select relevant sensors based on the users' preference for visual or kinesthetic MI. Recent functional magnetic resonance imaging research has shown that the different brain area activations can be used to determine movement from multivariate pattern analysis of the signal [60]. We have shown that high-density EEG--typically defined as EEG setups with 64 sensors or more [61]--is not essential for decoding MI and that this might even negatively affect decoding performance by introducing noise in the data. A relatively small number of electrodes could be used to decode MI from EEG signals, which also demonstrates robustness to the loss of faulty electrodes. This is most apparent when comparing the decoding performance on the full motor cortex subset and the MC reduced subset, which only uses half the number of sensors (i.e., half the spatial resolution) available for this brain region. The current research applied a top-down approach by removing sensors and subselecting based on existing datasets and devices. Future research could apply a bottom-up approach by starting from one sensor located over the primary motor cortex and including sensors that cover the regions of interest associated with MI [14]. Alternatively, fixing the number of sensors and evaluating different sensor locations could also provide insights into optimal EEG acquisition configurations. Additionally, investigating both intra- and inter-individual differences in decoding performance could also provide better insights into the sensor locations to include. By relating characteristics of the individual and possible internal or external properties to decoding performance, decoding models could take this variability into account. While there is variability in decoding accuracy between individuals, it seems to remain consistent for different sensor subsets within the same individual according to our results, as shown by Figure 7. Accordingly, we postulate that this approach could be used to determine the minimal number of sensors that are necessary for MI decoding. By using more advanced methods that also learn a representation of the data, such as deep learning, an optimal set of sensors could also be identified in a data-driven way. Previous research demonstrated that MI questionnaire results could be used to predict the performance of MI-based BCI [62]. Relating MIQ-3 questionnaire outcomes with decoding performance would therefore also be promising as a tool to determine the appropriate user training that would be necessary to reach reasonable decoding performance. By applying these methods, the current feedback runs could be expanded to develop a user-friendly and efficient calibration procedure for new users. This knowledge could inform the design of future EEG acquisition devices with the specific purpose of using them in MI-based BCI control systems. Recent advances in EEG sensor technology [63; 64; 65] could also improve the user-friendliness of EEG devices thanks to increased comfort and easy setup. Improved signal quality from a single electrode could also facilitate sensor reduction and is a future avenue of research. This also opens the possibility of using existing consumer-grade sensor devices for MI-based control such as OpenBCI ([[https://openbci.com/](https://openbci.com/)]([https://openbci.com/](https://openbci.com/)) (accessed on 23 February 2023)) or EMOTIV ([[https://www.emotiv.com/](https://www.emotiv.com/)]([https://www.emotiv.com/](https://www.emotiv.com/)) (accessed on 23 February 2023)) products, among others. Furthermore, including other biosignal modalities, such as in the upcoming Galea ([[https://galea.co/](https://galea.co/)]([https://galea.co/](https://galea.co/)) (accessed on 23 February 2023)) device, should result in a robust decoding system, bringing us one step closer to affordable and reliable BCI control.