Electroencephalogram (EEG) signals provide an objective physiological index for the identification of the driver's fatigue state. It is very important to choose appropriate channels and EEG signal features adaptively due to the features varying with different subjects and time. A support vector machine (SVM) based increasing feature selection algorithm for driving fatigue EEG classification is presented in this paper. The algorithm is a method to select EEG channels and features for driving fatigue adaptively in an ascending order. We can select the optimal feature each time from the remaining candidate features using the optimized SVM model minimum error rate as the index. The experimental calculation has characteristics of using 16 electrode channels which cover the whole head in the main area, of selecting 208 candidate features as the initial set, of selecting to the EEG data calculation recorded in 5 different time periods of a subject, and of choosing error rate of 2% as the algorithm termination condition. The selected features and models, therefore, can reach a high level of classification and generalization ability.
Algorithms ; Automobile Driving ; Electrodes ; Electroencephalography ; Fatigue ; Humans ; Support Vector Machine ; Time Factors