Identification of individuals using functional near-infrared spectroscopy based on a one-dimensional convolutional neural network
© 2024 Wiley‐VCH GmbH..
In recent years, the application of functional near-infrared spectroscopy (fNIRS) and deep learning techniques has emerged as a promising method for personal identification. In this study, we innovatively utilized a deep learning framework and fNIRS data for personal identification. The framework is a one-dimensional convolutional neural network (Conv1D) trained on resting-state fNIRS signals collected from the frontal cortex of adults. In data preprocessing, we employed a sliding window-based data augmentation technique and high-pass filter, which could result in the highest identification accuracy through multiple experiments. Based on a data set consisting of 56 adult participants, the identification accuracy of 90.36% is achieved for training data with a window size of approximately 4.62 s; with the increase in training data window size, the identification accuracy can reach (97.65 ± 2.35)%. Our results suggest that deep learning is valuable for fNIRS-based personal identification, with potential applications in security, biometrics, and healthcare.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Journal of biophotonics - 17(2024), 3 vom: 31. März, Seite e202300453 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Yichen [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 26.03.2024 Date Revised 26.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/jbio.202300453 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM367728583 |
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| 520 | |a In recent years, the application of functional near-infrared spectroscopy (fNIRS) and deep learning techniques has emerged as a promising method for personal identification. In this study, we innovatively utilized a deep learning framework and fNIRS data for personal identification. The framework is a one-dimensional convolutional neural network (Conv1D) trained on resting-state fNIRS signals collected from the frontal cortex of adults. In data preprocessing, we employed a sliding window-based data augmentation technique and high-pass filter, which could result in the highest identification accuracy through multiple experiments. Based on a data set consisting of 56 adult participants, the identification accuracy of 90.36% is achieved for training data with a window size of approximately 4.62 s; with the increase in training data window size, the identification accuracy can reach (97.65 ± 2.35)%. Our results suggest that deep learning is valuable for fNIRS-based personal identification, with potential applications in security, biometrics, and healthcare | ||
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