ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection

Xiaonei ZHANG; Wenpeng ZHAI; Huirang HOU; Qinghao MENG

doi:10.11999/JEIT200413

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(7): 2032-2037

Xiaonei ZHANG, Wenpeng ZHAI, Huirang HOU, Qinghao MENG. ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2032-2037. doi: 10.11999/JEIT200413

Citation:

Xiaonei ZHANG, Wenpeng ZHAI, Huirang HOU, Qinghao MENG. ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2032-2037. doi: 10.11999/JEIT200413

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ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection

doi: 10.11999/JEIT200413

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (61573253), The National Key R&D Program of China (2017YFC0306200)

Received Date: 2020-05-26
Rev Recd Date: 2020-11-23

Available Online: 2020-11-26

Publish Date: 2021-07-10

Abstract

Abstract

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.
- Olfactory ElectroEncephaloGram (EEG),
- Channel selection,
- Odor identification,
- ReliefF-Pearson

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References(18)

References

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