Implementing and deploying BCI control systems in the real world comes with numerous challenges [5]. The neural activity of a person can be influenced by several external factors, such as distractions or external electromagnetic fields from other electronic devices, which might obfuscate the patterns in brain activity related to the chosen control modality [6]. Additionally, EEG signals are different for each person, requiring the acquisition of a person's neural data before one can train an artificial intelligence method that is typically employed to decode neural activity [7]. The medical or research-grade sensor devices and computational resources that are currently necessary to decode the signals from these devices can become prohibitively expensive for an individual without financial support. Making BCI technology more accessible is a crucial step toward applying BCI in real-world scenarios. One possible way to limit both computational and monetary costs is to use commercial devices that have a smaller number of sensors [8]. Hence, it is of interest to identify the minimal number of sensors that is necessary to reliably decode user intention. Additionally, the optimal locations of the sensors are also important for EEG decoding. Depending on the EEG modality used to generate commands, a specific subset of sensors will be necessary. One of these modalities is motor imagery (MI), i.e., the neural activity related to motor planning in the execution of a movement, but also occurring with the imagination of a movement [9; 10]. Conventionally, MI is defined as \"the mental simulation of an action without the corresponding motor output\" [11]. There is currently a gap between BCI engineering research that mostly focuses on finding methods that can reliably decode MI from existing EEG data and neurophysiology studies that study the activation of the brain during MI. Multiple brain areas are activated when performing MI. Most activity occurs around the primary motor cortex [12; 13; 14; 15; 16]; however, numerous studies have determined that other areas of the brain are also activated during MI. These areas include the parietal cortex [13; 16; 17], the premotor cortex [11; 13; 14; 16], and even the cerebellum [13; 18]. Selecting the optimal set of EEG sensors is an important aspect of BCI research [19; 20]. Most research related to reducing the number of sensors attempts to identify the optimal user-specific subset for MI decoding [21; 22] or aims to use statistical methods to reduce the number of channels [23; 24]. Few studies have been conducted to experimentally verify the influence of sensor choice on decoding accuracy [25] and generally do not relate their results with neuroscientific research on the brain areas involved in MI. This research aims to experimentally identify the minimal subset of electrodes and their optimal locations for the purpose of MI classification by training different machine learning (ML) models on MI data. From existing literature on the functional regions of the brain [26], the optimal locations would be expected around the motor cortex area of the brain. However, since other sources of neural activity can also play a role in motor planning, they might be essential to include in the EEG inputs used to decode MI [13; 27]. Since performing an exhaustive search of all possible sensor subsets would be computationally unfeasible, we decided to perform an experimental evaluation of decoding performance when using specific sensor subsets, using common off-the-shelf EEG decoding methods. For this purpose, BCI decoding pipelines were trained on previously acquired MI data. The chosen sensor subsets were selected based on their usage in commercially available EEG devices or their involvement in MI. To validate that the data are qualitative and contain the information relevant to this analysis, visually evoked potentials (VEP [28; 29]), were first identified at pertinent sensor locations. VEPs were chosen for their strong activation and previous use in BCI [30], often in conjunction with MI [31].