ElectroEncephaloGram (EEG) signal detection and recognition is an important diagnostic method for the epilepsy. Radial Basis Function (RBF) neural network has excellent performance on approximation and generalization, and can directly recognize EEG signals in different states. However, its transparency and interpretability are low, and it also ignore the different separabilities between different classes of data. In this paper, a classification tree based on RBF neural networks and minimax probability decision technique is proposed, using one-against-one and exclusive method and paying much attention to the different separabilities among classes. Experiments on EEG signals show that the proposed method has clear structure, strong classification ability and better interpretability.
邓赵红,陈俊勇,刘解放,王士同. 面向癫痫脑电图信号识别的径向基最小最大概率分类树[J]. 电子与信息学报, 2016, 38(11): 2848-2855.
DENG Zhaohong, CHEN Junyong, LIU Jiefang, WANG Shitong. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition. JEIT, 2016, 38(11): 2848-2855.
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