Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain–computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an [Formula: see text]-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on [Formula: see text] optimized power-spectral-density features extracted from [Formula: see text] real electrodes and [Formula: see text] nonreal electrode’s features. Subsequently, the [Formula: see text] real electrodes’ sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen’s kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.

中文翻译：

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使用基于梯形差分的电极序列散列方法的嗅觉脑电信号分类

嗅觉诱发脑电图（EEG）信号分类在疾病治疗、神经科学研究、多媒体应用和脑机接口等多个领域具有重要意义。本文提出了一种基于梯形差分的电极序列哈希方法用于嗅觉脑电信号分类。首先，为每个脑电样本的每个频段构建一个[公式：见正文]层梯形特征集，其顶部、底部和高度的尺寸比为1:2:1。该构造基于从[公式：参见文本]真实电极和[公式：参见文本]非真实电极的特征中提取的[公式：参见文本]优化的功率谱密度特征。随后，[公式：见正文]构建的梯形特征集的每一层的真实电极序列（ES）代码是通过将特征值按升序排列得到的。最后，使用最近邻分类法找到一个类，其 ES 编码与测试样本的编码最相似。从 11 名受试者收集的 13 类嗅觉脑电图信号用于比较所提出方法与六种传统分类方法的分类性能。比较表明，该方法的平均准确率为 94.3%，Cohen 的 kappa 值为 0.94，精度为 95.0%，F1-measure 为 94.6%，均高于现有方法。最近邻分类用于寻找ES码与测试样本最相似的类。从 11 名受试者收集的 13 类嗅觉脑电图信号用于比较所提出方法与六种传统分类方法的分类性能。比较表明，该方法的平均准确率为 94.3%，Cohen 的 kappa 值为 0.94，精度为 95.0%，F1-measure 为 94.6%，均高于现有方法。最近邻分类用于寻找ES码与测试样本最相似的类。从 11 名受试者收集的 13 类嗅觉脑电图信号用于比较所提出方法与六种传统分类方法的分类性能。比较表明，该方法的平均准确率为 94.3%，Cohen 的 kappa 值为 0.94，精度为 95.0%，F1-measure 为 94.6%，均高于现有方法。