TY - JOUR
AU - 
AB - Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his/her pilot data to account for individual variability. Such approaches ca
TI - EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach
JF - EURASIP Journal on Advances in Signal Processing
DO - 10.1155/2008/519480
DA - 2009-01-20
UR - https://www.deepdyve.com/lp/wiley/eeg-based-subject-and-session-independent-drowsiness-detection-an-yTEo6pAbjb
VL - 2008
IS - 2008
DP - DeepDyve
ER -