Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials.

ABSTRACT: Objective.Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area's localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid.Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimoto

SUBMITTER: Asman P 

PROVIDER: S-EPMC8718352 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature