The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Automatic detection of drowsiness in EEG records based on multimodal analysis
Open accessAbstract
Drowsiness is one of the main causal factors in many traffic accidents due to the clear decline in the attention and recognition of danger drivers, diminishing vehicle-handling abilities. The aim of this research is to develop an automatic method to detect the drowsiness stage in EEG records using time, spectral and wavelet analysis. A total of 19 features were computed from only one EEG channel to differentiate the alertness and drowsiness stages. After a selection process based on lambda of Wilks criterion, 7 parameters were chosen to feed a Neural Network classifier. Eighteen EEG records were analyzed. The method gets 87.4% and 83.6% of alertness and drowsiness correct detections rates, respectively. The results obtained indicate that the parameters can differentiate both stages. The features are easy to calculate and can be obtained in real time. Those variables could be used in an automatic drowsiness detection system in vehicles, thereby decreasing the rate of accidents caused by sleepiness of the driver.
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Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Daubechies, I., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,631
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Rechtschaffen, A. (1968) · Medical Entomology and Zoology · Cited by 11,772
Independent Component Analysis of Electroencephalographic Data
Makeig, S., Bell, A. J., Jung, T., & B, T. J. S. A. (1995) · Cited by 1,957
Atlas of Electroencephalography
(1941) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1941.02820290075031 · Cited by 998
Estimating alertness from the EEG power spectrum
Jung, T.-P., Makeig, S., Stensmo, M., & Sejnowski, T. J. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.553713 · Cited by 480
EEG-based drowsiness estimation for safety driving using independent component analysis
Lin, C.-T., Wu, R.-C., Liang, S.-F., Chao, W.-H., Chen, Y.-J., & Jung, T.-P. (2005) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2005.857555 · Cited by 470
May, J. F., & Baldwin, C. L. (2009) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2008.11.005 · Cited by 466
Can SVM be used for automatic EEG detection of drowsiness during car driving?
Yeo, M. V. M., Li, X., Shen, K., & Wilder-Smith, E. P. V. (2009) · Safety Science · doi:10.1016/j.ssci.2008.01.007 · Cited by 368
Cited by
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
Aboalayon, K., Faezipour, M., Almuhammadi, W., & Moslehpour, S. (2016) · Entropy · doi:10.3390/e18090272 · Cited by 382
A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving
Lohani, M., Payne, B. R., & Strayer, D. L. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00057 · Cited by 348
A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems
Stancin, I., Cifrek, M., & Jovic, A. (2021) · Sensors · doi:10.3390/s21113786 · Cited by 269
Khosla, A., Khandnor, P., & Chand, T. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.02.002 · Cited by 267
A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems
Kartsch, V. J., Benatti, S., Schiavone, P. D., Rossi, D., & Benini, L. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.11.005 · Cited by 69
Metadata
- Authors
- Garcés Correa, A., Orosco, L., & Laciar, E.
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2013-08-20
- Volume
- 36
- Issue
- 2
- Pages
- 244-249
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W1965233495
- Cited by
- 275
- References
- 30
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Garcés Correa, A., Orosco, L., & Laciar, E. (2014). Automatic detection of drowsiness in EEG records based on multimodal analysis. Medical Engineering & Physics. https://doi.org/10.1016/j.medengphy.2013.07.011
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