Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification
Abstract Background Alertness plays a crucial role in the completion of important tasks. However, application of existing methods for evaluating alertness is limited due to issues such as high subjectivity, practice effect, susceptibility to interference, and complexity in data collection. Currently...
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BMC
2025-08-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01422-4 |
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| author | Yong Zhou Yizhou Tan Shasha Wang Hanshu Cai Ying Gu |
| author_facet | Yong Zhou Yizhou Tan Shasha Wang Hanshu Cai Ying Gu |
| author_sort | Yong Zhou |
| collection | DOAJ |
| description | Abstract Background Alertness plays a crucial role in the completion of important tasks. However, application of existing methods for evaluating alertness is limited due to issues such as high subjectivity, practice effect, susceptibility to interference, and complexity in data collection. Currently, there is an urgent need for a rapid, quantifiable, and easily implementable alertness assessment method. Methods Twelve optical stimulation frequencies ranged from 4 to 48 Hz were chosen to induce brainwave entrainment (BWE) for 30 s, respectively, in 40 subjects. Electroencephalogram (EEG) were recorded at the prefrontal pole electrodes Fpz, Fp1, and Fp2. Karolinska Sleepiness Scale, psychomotor vigilance test and β band power in resting EEG, were used to evaluate the alertness level before and after optical stimulation-induced BWE. The correlation between nine EEG features during the BWE and different alertness states were analyzed. Next, machine learning models including support vector machine, Naive Bayes and logistic regression were employed to conduct integrated analysis on the EEG features with significant differences. Results We found that BWE intensity, β band power, and γ band power exhibit significant differences across different states of alertness. The area under the receiver operating characteristic curve (AUC) of individual features for classifying alertness states was between 0.62–0.83. To further improve classification efficacy, these three features were used as input parameters in machine learning models. We found that Naive Bayes model showed the best classification efficacy in 30 Hz optical stimulation, with AUC reaching 0.90, an average accuracy of 0.90, an average sensitivity of 0.89, and an average specificity of 0.90. Meanwhile, we observed that the subjects’ alertness levels did not change significantly before and after optical stimulation-induced BWE. Conclusions Our study demonstrated that the use of machine learning to integrate EEG features during 30 s optical stimulation-induced BWE showed promising classification capabilities for alertness states. It provided a rapid, quantifiable, and easily implementable alertness assessment option. |
| format | Article |
| id | doaj-art-b024191aa5d849b2a48b400a36be53ae |
| institution | Kabale University |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj-art-b024191aa5d849b2a48b400a36be53ae2025-08-20T04:02:54ZengBMCBioMedical Engineering OnLine1475-925X2025-08-0124111810.1186/s12938-025-01422-4Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classificationYong Zhou0Yizhou Tan1Shasha Wang2Hanshu Cai3Ying Gu4The Chinese People’s Liberation Army Medical SchoolThe Department of Laser Medicine, The First Medical Center, Chinese PLA General HospitalThe School of Information Science and Engineering, Lanzhou UniversityThe School of Information Science and Engineering, Lanzhou UniversityThe Department of Laser Medicine, The First Medical Center, Chinese PLA General HospitalAbstract Background Alertness plays a crucial role in the completion of important tasks. However, application of existing methods for evaluating alertness is limited due to issues such as high subjectivity, practice effect, susceptibility to interference, and complexity in data collection. Currently, there is an urgent need for a rapid, quantifiable, and easily implementable alertness assessment method. Methods Twelve optical stimulation frequencies ranged from 4 to 48 Hz were chosen to induce brainwave entrainment (BWE) for 30 s, respectively, in 40 subjects. Electroencephalogram (EEG) were recorded at the prefrontal pole electrodes Fpz, Fp1, and Fp2. Karolinska Sleepiness Scale, psychomotor vigilance test and β band power in resting EEG, were used to evaluate the alertness level before and after optical stimulation-induced BWE. The correlation between nine EEG features during the BWE and different alertness states were analyzed. Next, machine learning models including support vector machine, Naive Bayes and logistic regression were employed to conduct integrated analysis on the EEG features with significant differences. Results We found that BWE intensity, β band power, and γ band power exhibit significant differences across different states of alertness. The area under the receiver operating characteristic curve (AUC) of individual features for classifying alertness states was between 0.62–0.83. To further improve classification efficacy, these three features were used as input parameters in machine learning models. We found that Naive Bayes model showed the best classification efficacy in 30 Hz optical stimulation, with AUC reaching 0.90, an average accuracy of 0.90, an average sensitivity of 0.89, and an average specificity of 0.90. Meanwhile, we observed that the subjects’ alertness levels did not change significantly before and after optical stimulation-induced BWE. Conclusions Our study demonstrated that the use of machine learning to integrate EEG features during 30 s optical stimulation-induced BWE showed promising classification capabilities for alertness states. It provided a rapid, quantifiable, and easily implementable alertness assessment option.https://doi.org/10.1186/s12938-025-01422-4AlertnessOptical stimulationBrainwave entrainmentElectroencephalogramMachine learningKarolinska Sleepiness Scale |
| spellingShingle | Yong Zhou Yizhou Tan Shasha Wang Hanshu Cai Ying Gu Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification BioMedical Engineering OnLine Alertness Optical stimulation Brainwave entrainment Electroencephalogram Machine learning Karolinska Sleepiness Scale |
| title | Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification |
| title_full | Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification |
| title_fullStr | Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification |
| title_full_unstemmed | Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification |
| title_short | Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification |
| title_sort | alertness assessment by optical stimulation induced brainwave entrainment through machine learning classification |
| topic | Alertness Optical stimulation Brainwave entrainment Electroencephalogram Machine learning Karolinska Sleepiness Scale |
| url | https://doi.org/10.1186/s12938-025-01422-4 |
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