A Multichannel Continuous Clinical Electromyography Dataset from Neurosurgery

Published: 13 February 2024| Version 2 | DOI: 10.17632/7hyptcbkkd.2
Wanting Ma, Lin Chen, Xiaofan Pang


In this dataset, we present a six-channel EMG dataset obtained from five cranial nerves during the cerebellopontine angle tumor surgery from 11 patients using the technology of cIONM. It’s the first dataset to automatically recognize or forecast EMG patterns during cIONM. We also spent time labeling and classifying the data so that the real intraoperative EMG data are more generalizable and clinically meaningful compared to the previous guided, fixed-posture EMG data performed in the laboratory. This dataset can be used to develop and validate more deep-learning-based or machine-learning-based algorithms, models, or tools for monitoring neurological function related to surgery and to study the mechanisms and effects of nerve injury or repair during surgery. In addition, the dataset can provide useful information for medical practice to improve the safety and success of surgery.



Electrophysiological Method in Neurobiology, Neurosurgery, Machine Learning, Electromyography, Clinical Electromyography, Intraoperative Monitoring, Biomedical Signal Processing, Deep Learning