Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
J Neural Eng. 2022 Jul 5;19(4). doi: 10.1088/1741-2552/ac7b4a.
The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels.In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search algorithm, including an opposition-based learning strategy for generating high-quality initial population, an adaptive parameter strategy for improving search capability, and a bitwise operation strategy for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels.With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy.The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.
嗅觉诱发电位脑电图（olfactory EEG）信号的分类在疾病诊断、情绪调节、多媒体等领域具有潜在的应用价值。为了实现高精度的分类，通常需要使用大量的 EEG 通道，但这也带来了信息冗余、过拟合和计算负载高等问题。因此，需要进行通道选择以找到并使用最有效的通道。
在本研究中，我们提出了一种多策略融合二进制和声搜索（MFBHS）算法，并将其与黎曼几何分类框架相结合，用于选择嗅觉 EEG 信号分类的最优通道集。MFBHS 是通过同时将三种策略集成到二进制和声搜索算法中而设计的，包括用于生成高质量初始种群的基于对立学习的策略、用于提高搜索能力的自适应参数策略以及用于保持种群多样性的位运算策略。它直接在 EEG 信号的协方差矩阵上进行通道选择，并使用新生成的通道子集的通道数量和由黎曼分类器计算的分类准确性来评估该子集。
基于两个公共嗅觉 EEG 数据集设计了五个不同的分类协议，评估了 MFBHS 的性能，并与一些最先进的算法进行了比较。实验结果表明，我们的方法可以在保持高分类准确性的同时，最小化通道数量，与使用所有通道的准确性相当。此外，MFBHS 通道选择的跨受试者泛化测试表明，通过训练获得的与受试者无关的通道可以直接用于未经训练的受试者，而不会大大降低分类准确性。
所提出的 MFBHS 算法是一种在嗅觉识别中有效利用 EEG 通道的实用技术。
