Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SocialCom.2013.124 | SocialCom |
Keywords | DocType | Citations |
support vector machine,traditional broadband,minimum eeg features,broadband alpha,compact set,eeg signal,accurate drowsiness detection,best mechanism,sub band feature,complex multifaceted process,broadband signal,best sub-band,feature extraction,learning artificial intelligence,electroencephalography,sleep,support vector machines | Conference | 4 |
PageRank | References | Authors |
0.50 | 3 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoda Yu | 1 | 4 | 0.50 |
| Peng Li | 2 | 1912 | 152.85 |
| Honghuang Lin | 3 | 29 | 3.83 |
| Ehsan Rohani | 4 | 12 | 2.76 |
| Gwan Choi | 5 | 369 | 56.66 |
| Botang Shao | 6 | 23 | 1.61 |
| Qian Wang | 7 | 4 | 0.50 |