基于ReliefF-Pearson的嗅觉脑电通道选择

ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection

基于脑电(EEG)信号的气味识别研究在嗅觉功能客观评价及嗅觉障碍疾病诊断等方面具有重要的应用价值。在实际应用场景中使用过多EEG通道会带来诸多不便,因此研究如何选择EEG通道尤为重要。该文针对嗅觉EEG信号分类中的通道选择问题,提出了一种新型的基于ReliefF-Pearson的嗅觉EEG通道选择算法。该算法结合ReliefF的权值思想和Pearson系数的相关性原理对EEG通道进行选择。结果表明,与传统基于ReliefF的通道选择算法相比,该文所提算法在保证一定分类准确率的同时能够显著减少使用的通道数量,并且通道选择的结果不依赖人为经验和分类器。此外,使用该方法获取的通道,其空间分布与已有的嗅觉神经生理学位置相一致,进一步证实了该方法的科学性和有效性。该文所提算法为嗅觉EEG通道选择的研究提供了新思路。

The study of odor recognition based on ElectroEncephaloGram(EEG)signals has important application value to objectively evaluating olfactory function and diagnosing olfactory disorders.Because of the inconvenience caused by using too many EEG channels in practical application scenarios,it is particularly important to study how to choose EEG channels.In this paper,a new ReliefF-Pearson channel selection algorithm is proposed to solve the channel selection problem in the classification of olfactory EEG signals.The algorithm combines the weight idea of ReliefF and the correlation principle of Pearson coefficient to select EEG channels.Experimental results show that compared with the traditional ReliefF-based channel selection algorithm,the proposed algorithm could significantly reduce the number of channels used while ensuring a certain classification accuracy,and the result of channel selection does not depend on human experience and classifiers.In addition,the spatial distribution of the selected channels is consistent with the existing olfactory neurophysiological position,which further confirms the scientificity and effectiveness of this method.The proposed method provides new idea for the research of olfactory EEG channel selection.