TY  - JOUR
T1  - EEG Spectrum Analysis of Various Electrodes from Sleep Stages of Detection and Drowsiness with Monitoring Driving Performance of Estimation Control System
AU - Saravanamoorthi, A. AU - Banu, R.S.D. Wahidha 
JO  - Asian Journal of Information Technology
VL  - 13
IS  - 10
SP  - 618
EP  - 626
PY  - 2014
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2014.618.626
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2014.618.626
KW  - Alertness
KW  -EEG
KW  -power spectrum
KW  -spectrum analysis
KW  -Autoregressive (AR) Model
KW  -Linear Regression Model
AB  - The growing number of traffic accidents in resent years has become a serious concern to society. Accidents caused by driver’s drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver’s abilities of perception, recognition and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver’s abilities of perception, recognition and vehicle control abilities; this study proposes methods for drowsiness estimation that combine the Electroencephalogram (EEG) log sub band power spectrum, correlation analysis, principal component analysis, Autoregressive (AR) Model and Liner Regression Models to indirectly estimate driver’s drowsiness level in a virtual-reality-based driving simulator. Results show that it is feasible to quantitatively monitor driver’s alertness with concurrent changes in driving performance in a realistic driving simulator.
ER  -