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Clinical Brain-Computer Interface (BCI) systems seek to enable paralyzed individuals to operate devices with their brain activity. Non-invasive systems based on electroen-cephalographic (EEG) signals are popular since they avoid risks associated with invasive procedures, but unfortunately EEG signals are inherently noisy, making effective classifiers challenging to develop. Commonly, new classifiers are benchmarked on signals from healthy subjects executing physical movements, under the assumption that the performance will transfer to clinical cases where only imagined movements are possible. Here, we show in contrast that classifiers trained on signals associated with actual movements perform erratically when applied to signals associated with imagined movements. We suggest that this is because the signals lay in different domains. Then, to exploit the different statistical distributions, we apply a domain adaptation technique, Frustratingly Easy Domain Adaptation (FEDA), improving classifier performance accuracy by a third on a discrimination task that simulates the clinical condition.