Abstract
Continuous Intraoperative Neurophysiologic Monitoring (cIONM) is a widely used technology to improve surgical outcomes and prevent cranial nerve injury during skull base surgery. Monitoring of free-running electromyogram (EMG) plays an important role in cIONM, which can be used to identify different discharge patterns, alert the surgeon to potential nerve damage promptly, etc. In this dataset, we collected clinical multichannel EMG signals from 11 independent patients' data using a Neuromaster G1 MEE-2000 system (Nihon Kohden, Inc., Tokyo, Japan). Through innovative classification methods, these signals were categorized into seven different categories. Remarkably, channel 1 and channel 2 captured continuous EMG signals from the facial nerve (VII cranial nerve), while channel 3 to channel 6 focused on V, XI, X, and XII cranial nerves. This is the first time that intraoperative EMG signals have been collated and presented as a dataset and labelled by professional neurophysiologists. These data can be utilized to develop the architecture of neural networks in deep learning, machine learning, pattern recognition, and other commonly employed biomedical engineering research methods, thereby providing valuable information to enhance the safety and efficacy of surgical procedures.