Movement-Related Cortical Potentials appear before movement execution/intention and have been proposed for the control of brain-computer interfaces. In this work, the detection accuracy of hand movement execution from time intervals where MRCPs are expected is investigated, using a publicly available dataset, three montages and amplitude features with or without xDAWN filtering. The montages were: 1) The 31-channel montage used by the dataset authors (HD), 2) a montage of 6 channels covering the motor area (LD), and a Hybrid setup comprising the LD and 4 electro-oculography channels. The classifier used was shrinkage Linear Discriminant Analysis. The accuracy scores for the 2×3 factors were tested using rmANOVA, with sphericity corrections where necessary and Holm post-hoc tests; sensitivity, specificity, F1-score, and AUC are additionally reported for completeness. Both the montage and its interaction with the xDAWN filtering had a significant effect on the achieved accuracies (p < 0.05), with the HD and Hybrid montage outperforming the 6-channel subset. We thus conclude that hand movement execution detection benefits from denser montages and electro-oculography information.