Kalb, S.; Böck, C.; Bolz, M.; Schlömmer, C.; Kudumija, L.; Dünser, M.W.; Meier, J. Continuous Detection of Subtle Differences in Chromatic Visually Evoked Potentials Applied to Closed Eyes in Healthy Volunteers. Preprints2024, 2024041774. https://doi.org/10.20944/preprints202404.1774.v1
APA Style
Kalb, S., Böck, C., Bolz, M., Schlömmer, C., Kudumija, L., Dünser, M.W., & Meier, J. (2024). Continuous Detection of Subtle Differences in Chromatic Visually Evoked Potentials Applied to Closed Eyes in Healthy Volunteers. Preprints. https://doi.org/10.20944/preprints202404.1774.v1
Chicago/Turabian Style
Kalb, S., Martin Wolfgang Dünser and Jens Meier. 2024 "Continuous Detection of Subtle Differences in Chromatic Visually Evoked Potentials Applied to Closed Eyes in Healthy Volunteers" Preprints. https://doi.org/10.20944/preprints202404.1774.v1
Abstract
Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulation, and between red or green light stimulation in awake volunteers with closed eyelids. This new method of EEG analysis is visionary in the understanding of visual signal processing and will facilitate anaesthetic depth research. Methods: X-gradient boosting models were used to classify the cortical response to VEP stimulation (no light vs. any light stimulation; red vs. green light stimulation). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), training across all but one participant with prediction in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulation (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96±0.05, accuracyindividual: 0.94±0.05; AUCROConeout: 0.98±0.04; accuracyo-neout:0.96±0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between green and red colour stimulation (AUCROCall: 0.97; accuracyall: 0.91; AUCROCin-dividual: 0.98±0.04, accuracyindividual: 0.96±0.04; AUCROConeout: 0.96±0.05; accuracyoneout: 0.93±0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or any light stimulation and between green or red colour stimulation using VEPs in awake female and male volunteers with eyes closed. Our findings may open new possibilities for using VEPs in the intraoperative setting.
Keywords
visual evoked potentials; chromatic; red light; green light; eyelids closed; machine learning
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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