Near-infrared Spectrum Brain Pattern Recognition Based on One-dimensional Convolutional Neural Network

Differences between individual dynamic functional networks have stable spatial distributions, and whether such differences can be used for identification has not been fully studied. In this paper based on a set of public near-infrared spectroscopy datasets, we preprocessed the data of 30 subjects, c...

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Bibliographic Details
Published inChinese Control Conference pp. 8218 - 8223
Main Authors Yan, Xinyue, Feng, Yiyu, Zhang, Xianfu
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Subjects
Biological system modeling
Brain modeling
Classification algorithms
Convolutional neural network
Deep learning
Functional networks
Graphical models
Identity recognition
Machine learning
Pattern recognition
Predictive models
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Summary:Differences between individual dynamic functional networks have stable spatial distributions, and whether such differences can be used for identification has not been fully studied. In this paper based on a set of public near-infrared spectroscopy datasets, we preprocessed the data of 30 subjects, constructed brain networks under different time windows, and identified them as "brainprint" features. Next, we compared the impact of traditional classification algorithms and deep learning classification algorithms on "brainprint" recognition results. For the 30 classification predictions of different feature experimental groups, the one-dimensional convolutional neural network (1DCNN) model constructed in this paper can achieve an average recognition accuracy of 98.67 \%. This shows that the CNN model constructed in this study is feasible for the classification of near-infrared "brainprint" and provides a potential biometric technology for future identity recognition.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662103