Adaptive feature extractions in an EEG-based alertness estimation system

• Published in: 2005 IEEE International Conference on Systems, Man and Cybernetics Vol. 3; pp. 2096 - 2101 Vol. 3
• Main Authors: Chin-Teng Lin, Wen-Hung Chao, Yu-Chieh Chen, Sheng-Fu Liang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Adaptive feature selection; Automatic control; Cognition; correlation analysis; EEG; Feature extraction; Frequency estimation; Independent component analysis; Linear regression; neural fuzzy network; Security; Time frequency analysis; Vehicle safety; virtual reality; Wheels
• ISBN: 9780780392984; 0780392981
• ISSN: 1062-922X
• DOI: 10.1109/ICSMC.2005.1571458

During the past years, the public security has become an important issue, especially, the safe manipulation and control of various vehicles. Maintaining high cognition is particularly important for the drivers behind the steering wheel. It requires an optimal estimation system to online continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control. In this paper, we proposed an EEG-based alertness estimation system with automatic feature selection mechanism. The independent component analysis (ICA) is used first to decompose the measured electroencephalogram (EEG). Then, a time-frequency analysis is performed to evaluate the time-frequency characteristic of each ICA component. We also proposed a new adaptive feature extracting mechanism for selections of frequency bands and ICA components. Different ranges of the alpha rhythm of subjects can be evaluated by the adaptive feature extracting mechanism according to the correlation coefficient between the ICA time-frequency response and the driving performance. The extracted features are then trained both by linear regression model and self-constructing neuro-fuzzy inference network (SONFIN) for the estimation of driving performance. The training and testing results of SONFIN are 96% and 91%, while the results of linear regression model are 90% and 85%, respectively. It demonstrates that the proposed adaptive feature extracting mechanism can achieve a great performance in alertness estimation with frequency band and component selection.