Continuous Detection of Stimulus Brightness Differences Using Visual Evoked Potentials in Healthy Volunteers with Closed Eyes

TL;DR

This study shows that machine learning can detect differences in brain responses to light brightness using EEG, even with closed eyes.

Contribution

A novel method using machine learning to distinguish visual evoked potentials for varying light brightness in awake individuals with closed eyes.

Findings

Machine learning models achieved high accuracy in distinguishing between no light and light stimulation EEG responses.

The models also effectively differentiated between light stimulations of different brightness levels.

Performance was consistent across genders and participant-specific training scenarios.

Abstract

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulations, and between light stimulations with different brightnesses in awake volunteers with closed eyelids. This new method utilizing EEG analysis is visionary in the understanding of visual signal processing and will facilitate the deepening of our knowledge concerning anesthetic research. Methods: X-gradient boosting models were used to classify the cortical response to visual stimulation (no light vs. light stimulations and two lights with different brightnesses). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), and training across all but one participant with prediction performed 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 stimulations (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96 ± 0.05, accuracyindividual: 0.94 ± 0.05; AUCROConeout: 0.98 ± 0.04; accuracyoneout: 0.96 ± 0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between light stimulations with different brightnesses (AUCROCall: 0.97; accuracyall: 0.91; AUCROCindividual: 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 light stimulations using visual evoked potentials in awake female and male volunteers with eyes closed. Our findings may open new possibilities for the use of visual evoked potentials in the clinical and intraoperative setting.

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