Electroencephalography-based brain-computer interface (EEG-BCI) systems have been developed to enable individuals with physical disabilities to control external devices using their thoughts. The performance of EEG-BCI systems can be affected by various factors, including the choice of EEG feature representation. Contrastive learning is a machine learning approach that involves training a model to learn a discriminative representation that captures the underlying patterns in the data. This method has been shown to be effective for learning representations in a variety of domains, including computer vision, natural language processing, and speech recognition. This process encourages the model to learn features that capture the essential information needed to distinguish between different classes while ignoring the irrelevant information. In this study, we propose a contrastive learning approach to learn discriminative representations of EEG signals in the context of MI tasks. Specifically, we investigate the use of different EEG signal representations based on symmetric positive definite (SPD) matrix: covariance matrix, augmented covariance matrix, and coherence matrix. To achieve this, we process the different representation using the SPDNet architecture, which is designed to process SPD matrices. The main advantage of contrastive learning is that it can learn representations that are invariant to irrelevant variations, such as differences in electrode placement, noise, and individual differences in brain activity. By learning to contrast similar and dissimilar examples, contrastive learning can discover patterns that are shared across subjects and generalize well to new data. We validate our approach using the MOABB package, focusing on Cross Subject evaluation. In this sense contrastive learning can be used to learn meaningful representations of EEG signals from different subjects. The study is in its initial stages and further research is required to fully explore the potential of contrastive learning and SPDNet in the context of EEG-BCI.