EEG-Based Fatigue Classification by Using Parallel Hidden Markov Model and Pattern Classifier Combination

Abstract. Fatigue is the most important reason leading to traffic accidents. In order to ensure traffic safety, various methods based on electroencephalogram (EEG) are proposed. But most of them, either regression or classification, are focused on the relationship between fea-ture space and observation values, so the changing patterns of features are ignored or discarded. In this paper, we propose a new fatigue clas-sification method by using parallel hidden-Markov-model and pattern classifier combination techniques, where each model represents a partic-ular fatigue-high-related feature. In the experiment, subjects are asked to accomplish some simple tasks, and both their fatigue states and their EEG signals are recorded simultaneously. Experimental results indicate that the mean error rate obtained by using our new method are 11.15% for classifying 3 states and 16.91 % for classifying 4 states, respectively, while the existing approach can only reach 16.45 % and 23.55 % under the same condition