DocumentCode :
660869
Title :
Support Vector Machine Based Detection of Drowsiness Using Minimum EEG Features
Author :
Shaoda Yu ; Peng Li ; Honghuang Lin ; Rohani, Ehsan ; Gwan Choi ; Botang Shao ; Qian Wang
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
fYear :
2013
fDate :
8-14 Sept. 2013
Firstpage :
827
Lastpage :
835
Abstract :
Drowsiness presents major safety concerns for tasks that require long periods of focus and alertness. While there is a body of work on drowsiness detection using EEG signals in neuroscience and engineering, there exist unanswered questions pertaining to the best mechanisms to use for detecting drowsiness. Targeting a range of practical safety-awareness applications, this study adopts a machine learning based approach to build support vector machine (SVM) classifiers to distinguish between awake and drowsy states. While broadband alpha, beta, delta, and theta waves are often used as features in the existing work, lack of widely agreed precise definitions of such broadband signals and difficulty in accounting for interpersonal variability has led to poor classification performance as demonstrated in this study. Furthermore, the transition from wakefulness to drowsiness and deeper sleep stages is a complex multifaceted process. The richness of this process calls for inclusion of sub-band features for more accurate drowsiness detection. To shed light on the effectiveness of sub-banding, we quantitatively compare the performances of a large set of SVM classifiers trained upon a varying number of 1Hz sub band features. More importantly, we identify a compact set of neuroscientifcally motivated EEG features and demonstrate that the resulting classifier not only outperforms traditional broadband based classifiers but also is on a par with or superior than the best sub-band classifiers found by thorough search in a large space of 1Hz sub band features.
Keywords :
electroencephalography; feature extraction; learning (artificial intelligence); medical signal detection; signal classification; sleep; support vector machines; EEG signals; SVM classifiers; alpha waves; awake state; beta waves; broadband based classifiers; complex multifaceted process; deeper sleep stages; delta waves; drowsy state; interpersonal variability; machine learning based approach; minimum EEG features; neuroscience; neuroscientifcally motivated EEG features; safety-awareness applications; subband features; support vector machine based drowsiness detection; support vector machine classifiers; theta waves; Broadband communication; Electroencephalography; Feature extraction; Sleep; Support vector machines; Testing; Training; Drowsiness; EEG; Feature selection; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Computing (SocialCom), 2013 International Conference on
Conference_Location :
Alexandria, VA
Type :
conf
DOI :
10.1109/SocialCom.2013.124
Filename :
6693421
Link To Document :
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