Real-Time Intraoperative Sensorimotor Cortex Localization and Consciousness Assessment with the Spatial and Spectral Profile of the Median Nerve Somatosensory Evoked Potentials

Mapping the sensorimotor cortex is a critical first step in various applications, including awake craniotomies and invasive brain-computer interface systems. Usually, during surgery, the sensorimotor area is defined using the conventional phase reversal of the somatosensory evoked potentials (SSEPs) originating from median nerve stimulation. The approach relies on the subjective interpretation of the phase reversal amplitude captured with a strip electrode. However, the localized nature of the SSEP around the hand area, location, size of the craniotomy, and changes in brain activity due to tumor invasion or lesion can cause misconstrued interpretation of the phase reversal. In this study, using a high-density grid, we recorded electrocorticogram (ECoG) from the sensorimotor cortex of patients in the anesthetized and awake states. We used the spatial distributions of SSEPs in the temporal and spectral domain and employed an unsupervised machine learning approach to delineate the central sulcus in real time. In Simulink/Matlab, we visualized instantaneous signal amplitude and power modulations in the gamma band over the 3D cortical surface rendered from individual patient MRIs. Furthermore, we showed that the temporal and spectral features of the SSEPs can serve as a valuable tool for assessing consciousness. Specifically, using the long latency gamma modulations in the SSEP trace and the cortical interpeak latency, we show that we can differentiate between the anesthetized and awake states. Our findings have various potential implications ranging from intraoperative surgical planning to assessing the consciousness status of patients with disorders of consciousness.