Among all the operations with general anesthesia worldwide, some patients have had the terrible experience of Accidental Awareness during General Anesthesia (AAGA), an unexpected awakening during surgery [1], potentially leading to pain, recall of surgical events and post-traumatic stress disorders [2]. The inability to predict AAGA using only clinical signs, leads to the use of brain activity monitors [3]. Given AAGA patients’ first reflex to move, impeded by neuromuscular-blocking agents, we propose using a new Brain Computer Interface (BCI) with Median Nerve Stimulation (MNS) to detect their movement intentions (MI). Combining MI and MNS significantly impacts MNS-generated patterns, leading to better MI detection accuracy. MNS also provides a trigger, resulting in better classification (+18%) compared to asynchronous BCI [4]. To continue in this direction, we analyzed the MNS patterns of 13 patients receiving propofol, an intravenous anesthetic, during surgery at CHU Brugmann, Belgium [5]. Two conditions were examined: EEG activities of preoperative MNS while the patient is awake (MNS-awake), and intraoperative MNS during various stages of anesthesia (MNS-anesthesia). Then, a Riemannian Minimum Distance to the Mean classifier achieved 97% test balanced accuracy in distinguishing awake and anesthetized states. We also observed the classifier’s response evolution with anesthesia depth, in terms of distance between the MNS-awake class centroid and covariance matrices of other trials throughout the surgery (Fig. 1). This distance appears to track the patients’ awareness level: it is greater during the maintenance phase, at deeper anesthetic concentrations, than during the induction or emergence phases. This holds potential for creating a one class classifier using this distance, only relying on MNS-awake data, as MNS-anesthesia data are usually unavailable for calibration during regular surgeries.