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DimensionsTarafder, S. and Badruddin, N. and Yahya, N. and Egambaram, A. (2021) EEG-based Drowsiness Detection from Ocular Indices Using Ensemble Classification. In: UNSPECIFIED.
Full text not available from this repository.Abstract
One of the reasons for fatal road accidents is sleeping behind the wheel, and there are numerous methods developed to prevent these accidents. The proposed method uses a MATLAB-based BLINKER algorithm to extract ocular characteristics from EEG signals. The classification model makes this method unique to detect drivers' drowsiness using eye blinks. The decision tree model is used in feature selection and a Bayesian optimized ensemble bagged classifier for the prediction. The predictive classification model gives 88.1 accuracy. © 2021 ECBIOS 2021. All rights reserved.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | cited By 1; Conference of 3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021 ; Conference Date: 28 May 2021 Through 30 May 2021; Conference Code:176796 |
| Uncontrolled Keywords: | Electroencephalography; Highway accidents, Blinker; Classification models; Decision-tree model; Driver drowsiness; Drowsiness detection; EEG signals; Ensemble classification; Eye blink; Fatal road accidents; Features selection, Decision trees |
| Depositing User: | Mr Ahmad Suhairi UTP |
| Date Deposited: | 10 Nov 2023 03:30 |
| Last Modified: | 10 Nov 2023 03:30 |
| URI: | https://khub.utp.edu.my/scholars/id/eprint/15427 |
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