Authors:
Griet Goovaerts
1
;
Ad Denissen
2
;
Milica Milosevic
1
;
Geert van Boxtel
3
and
Sabine Van Huffel
1
Affiliations:
1
KU Leuven and iMinds, Belgium
;
2
Philips Research, Netherlands
;
3
Tilburg University, Netherlands
Keyword(s):
EEG, Drowsiness Detection, Blind Source Separation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Monitoring and Telemetry
Abstract:
Drowsiness is a serious problem for drivers which causes many accidents every day. It is estimated that drowsiness
is the cause of four deaths and 100 injuries per day in the United States. In this paper two methods have
been developed to detect drowsiness based on features of ocular artifacts in EEG signals. The ocular artifacts
are derived from the EEG signals by using Canonical Correlation Analysis (BSS-CCA). Wavelet transforms
are used to automatically select components containing eye blinks. Sixteen features are then calculated from
the eye blink and used for drowsiness detection. The first method is based on linear regression, the second on
fuzzy detection. For the first method, the drowsiness level is correctly detected in 72% of the epochs. The second
method uses fuzzy detection and detects the drowsiness correctly in 65% of the epochs. The best results
are obtained when using one single eye blink feature.