Please use this identifier to cite or link to this item:
|Title:||EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate|
|Authors:||Shen, K.-Q. |
Support vector machines (SVM)
|Citation:||Shen, K.-Q., Ong, C.-J., Shao, S.-Y., Li, X.-P., Wilder-Smith, E.P.V. (2008). EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate. Clinical Neurophysiology 119 (7) : 1524-1533. ScholarBank@NUS Repository. https://doi.org/10.1016/j.clinph.2008.03.012|
|Abstract:||Objective: Automatic measurement and monitoring of mental fatigue are invaluable for preventing mental-fatigue related accidents. We test an EEG-based mental-fatigue monitoring system using a probabilistic-based support vector-machines (SVM) method. Methods: Ten subjects underwent 25-h sleep deprivation experiments with EEG monitoring. EEG data were segmented into 3-s long epochs and manually classified into 5 mental-fatigue levels, based on subjects' performance on an auditory vigilance task (AVT). Probabilistic-based multi-class SVM and standard multi-class SVM were compared as classifiers for distinguishing mental fatigue into the 5 mental-fatigue levels. Results: Accuracy of the probabilistic-based multi-class SVM was 87.2%, compared to 85.4% using the standard multi-class SVM. Using confidence estimates aggregation, accuracy increased to 91.2%. Conclusions: Probabilistic-based multi-class SVM not only gives superior classification accuracy but also provides a valuable estimate of confidence in the prediction of mental fatigue level in a given 3-s EEG epoch. Significance: The work demonstrates the feasibility of an automatic EEG method for assessing and monitoring of mental fatigue. Future applications of this include traffic safety and other domains where measurement or monitoring of mental fatigue is crucial. © 2008 International Federation of Clinical Neurophysiology.|
|Source Title:||Clinical Neurophysiology|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 15, 2018
WEB OF SCIENCETM
checked on Oct 8, 2018
checked on Oct 21, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.