Detecting motor evoked potentials using neural convolutional networks: overcoming the limitations of manual analysis

Motor evoked potentials (MEPs) are electrophysiological signals of crucial diagnostic and monitoring importance in neurology, neurosurgery, and rehabilitation medicine. Traditionally, feature extraction from MEP data has been based on manual control and measurements performed by trained clinicians according to established rules, a process that is inherently subjective, time-consuming, and subject to significant differences between observers. This article provides a comprehensive rationale for using convolutional neural network (CNN)-based approaches to extract MEP features. CNNs provide superior performance in key parameters, including accuracy, reproducibility, processing speed, and the ability to detect hidden morphological patterns that may escape human visual perception, compared to traditional manual methods. In addition, automated CNN-based analysis eliminates the variability between patients, allowing for real-time intraoperative monitoring. Performance estimates based on computer modeling and a structured comparative analysis of the two methods strongly confirm this statement. The introduction of CNNs represents a revolutionary step towards objective, scalable, and clinically reliable analysis that can standardize the interpretation of MEP in a variety of clinical settings and potentially improve patient outcomes through more consistent neurological assessment.

1. Rossini P.M., Burke D., Chen R., et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee. Clinical Neurophysiology. 2015;126(6):1071–1107. https://doi.org/10.1016/j.clinph.2015.02.001

2. MacDonald D.B., Skinner S., Shils J., Yingling C. Intraoperative motor evoked potential monitoring – А position statement by the American Society of Neurophysiological Monitoring. Clinical Neurophysiology. 2013;124(12):2291–2316. https://doi.org/10.1016/j.clinph.2013.07.025

3. Picht Th., Mularski S., Kuehn B., et al. Navigated transcranial magnetic stimulation for preoperative functional diagnostics in brain tumor surgery. Neurosurgery. 2009;65(6):93–98. https://doi.org/10.1227/01.NEU.0000348009.22750.59

4. Kerwin L.J., Keller C.J., Wu W., Narayan M., Etkin A. Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials. Brain Stimulation. 2018;11(3):536–544. https://doi.org/10.1016/j.brs.2017.12.010

5. LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015;521:436–444. https://doi.org/10.1038/nature14539

6. Roy Y., Banville H., Albuquerque I., et al. Deep learning-based electroencephalography analysis: a systematic review. Journal of Neural Engineering. 2019;16. https://doi.org/10.1088/1741-2552/ab260c

7. Schirrmeister R.T., Springenberg J.T., Fiederer L.D.J., et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping. 2017;38(11):5391–5420. https://doi.org/10.1002/hbm.23730

8. Parduzi Q., Wermelinger J., Koller S.D., et al. Explainable AI for Intraoperative Motor-Evoked Potential Muscle Classification in Neurosurgery: Bicentric Retrospective Study. Journal of Medical Internet Research. 2025;27. https://doi.org/10.2196/63937

9. Kołodziej M., Majkowski A., Rak R.J., Wiszniewski P. Convolutional Neural Network-Based Classification of Steady-State Visually Evoked Potentials with Limited Training Data. Applied Sciences. 2023;13(24). https://doi.org/10.3390/app132413350

10. Bengio Y., Courville A., Vincent P. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;35(8):1798–1828. https://doi.org/10.1109/TPAMI.2013.50

11. Negro F., Muceli S., Castronovo A.M., Holobar A., Farina D. Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. Journal of Neural Engineering. 2016;13. https://doi.org/10.1088/1741-2560/13/2/026027

12. Craik A., He Y., Contreras-Vidal J.L. Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of Neural Engineering. 2019;16. https://doi.org/10.1088/1741-2552/ab0ab5

13. Wang Ch., Qian W., Shen S., et al. Enhanced semi-supervised model for acoustic leak detection in water distribution networks. Automation in Construction. 2025;175. https://doi.org/10.1016/j.autcon.2025.106228

14. He K., Zhang X., Ren Sh., Sun J. Deep Residual Learning for Image Recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016, Las Vegas, NV, USA. IEEE; 2016. P. 770–778. https://doi.org/10.1109/CVPR.2016.90

15. Selvaraju R.R., Cogswell M., Das A., et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), 22–29 October 2017, Venice, Italy. IEEE; 2017. P. 618–626. https://doi.org/10.1109/ICCV.2017.74