Re-purposing EEG Artifacts: Eye Blink Artifact Features as Predictors of Drowsiness Using Deep Learning.
Saved in:
| Title: | Re-purposing EEG Artifacts: Eye Blink Artifact Features as Predictors of Drowsiness Using Deep Learning. |
|---|---|
| Authors: | Egambaram, Ashvaany1,2 (AUTHOR) ashvaany.egambaram@utp.edu.my, Badruddin, Nasreen1,2 (AUTHOR), Yahya, Norashikin1,2 (AUTHOR) |
| Source: | Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-20. 20p. |
| Subjects: | Drowsiness, Deep learning, Long short-term memory, Blinking (Physiology), Traffic safety, Electroencephalography, Feature extraction, Machine learning |
| Abstract: | Drowsiness has emerged as a significant factor to traffic accidents, workplace accidents and fatalities. In efforts to detect drowsiness in drivers and site workers, researchers have increasingly utilized eye/eyelid images or spectral information from electroencephalograms (EEG). Traditional drowsiness detection systems rely on EEG spectral analysis or camera-based eye tracking, both of which require either clean EEG data or complex hardware setups. In contrast, this study rethinks conventional EEG pre-processing by repurposing eye blink artifacts, typically discarded as noise, as meaningful indicators of drowsiness. This can complement spectral analysis or external cameras/EOG, thereby simplifying the hardware configurations. Eye blink artifact features extracted from two public EEG datasets (nearly 16,000 eye blink events) are smoothed and averaged to enhance the quality of the feature. Seven deep learning models, 1D CNN, MLP, RNN, LSTM, BiLSTM, GRU, and BiGRU, were trained, validated, tested on unseen data, and then optimized using Keras Tuner algorithms to enhance performance. Bi-LSTM achieved the best performance in both datasets, with accuracy exceeding 95%, and precision, recall, AUC-ROC, and F1 score exceeding 96%. These findings suggest that the features of eye blink artifacts offer a viable alternative or can complement conventional EEG spectral analysis to improve drowsiness detection. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 189934117 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Re-purposing EEG Artifacts: Eye Blink Artifact Features as Predictors of Drowsiness Using Deep Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Egambaram%2C+Ashvaany%22">Egambaram, Ashvaany</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ashvaany.egambaram@utp.edu.my</i><br /><searchLink fieldCode="AR" term="%22Badruddin%2C+Nasreen%22">Badruddin, Nasreen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yahya%2C+Norashikin%22">Yahya, Norashikin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. Dec2025, Vol. 39 Issue 1, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Drowsiness%22">Drowsiness</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Blinking+%28Physiology%29%22">Blinking (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Drowsiness has emerged as a significant factor to traffic accidents, workplace accidents and fatalities. In efforts to detect drowsiness in drivers and site workers, researchers have increasingly utilized eye/eyelid images or spectral information from electroencephalograms (EEG). Traditional drowsiness detection systems rely on EEG spectral analysis or camera-based eye tracking, both of which require either clean EEG data or complex hardware setups. In contrast, this study rethinks conventional EEG pre-processing by repurposing eye blink artifacts, typically discarded as noise, as meaningful indicators of drowsiness. This can complement spectral analysis or external cameras/EOG, thereby simplifying the hardware configurations. Eye blink artifact features extracted from two public EEG datasets (nearly 16,000 eye blink events) are smoothed and averaged to enhance the quality of the feature. Seven deep learning models, 1D CNN, MLP, RNN, LSTM, BiLSTM, GRU, and BiGRU, were trained, validated, tested on unseen data, and then optimized using Keras Tuner algorithms to enhance performance. Bi-LSTM achieved the best performance in both datasets, with accuracy exceeding 95%, and precision, recall, AUC-ROC, and F1 score exceeding 96%. These findings suggest that the features of eye blink artifacts offer a viable alternative or can complement conventional EEG spectral analysis to improve drowsiness detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189934117 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08839514.2025.2587985 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Drowsiness Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Blinking (Physiology) Type: general – SubjectFull: Traffic safety Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Re-purposing EEG Artifacts: Eye Blink Artifact Features as Predictors of Drowsiness Using Deep Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Egambaram, Ashvaany – PersonEntity: Name: NameFull: Badruddin, Nasreen – PersonEntity: Name: NameFull: Yahya, Norashikin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08839514 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Applied Artificial Intelligence Type: main |
| ResultId | 1 |
