Towards Riemannian EEG classifiers to detect awake and anesthetized states using median nerve stimulation
Résumé
Among all the operations carried out under general anesthesia worldwide, some patients have had the terrible experience of Accidental Awareness during General Anesthesia (AAGA), an unexpected awakening during the surgical procedure. The inability to predict and prevent AAGA before its occurrence using only conventional measures, such as clinical signs, leads to the use of brain activity monitors. Given AAGA patients’first reflex to move, impeded by neuromuscular-blocking agents, we propose using a new Brain Computer Interface with Median Nerve Stimulation (MNS) to detect their movement intentions, specifically in the context of general anesthesia. Indeed, MNS induces movement-related EEG patterns, improving the detection of such intentions. In this article, we compared MNS effects on the motor cortex before and during surgery under general anesthesia. Then, a Riemannian Minimum Distance to the Mean classifier achieved 97% test balanced accuracy in distinguishing awake and anesthetized states. Additionally, we observed how the classifier’s response evolves with anesthesia depth, in terms of distance to the awake class centroid. This distance appears to track the patients’ awareness level during surgery. This holds promises for developing a future one-class classifier using only awake EEG data, as anesthesia EEG data are usually unavailable for classifier training, to detect AAGA.
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