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27 November 2023 A revised inception-ResNet model-based transfer learning for cross-subject decoding of fNIRS-BCI
Yao Zhang, Feng Gao
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Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 127703I (2023) https://doi.org/10.1117/12.2689271
Event: SPIE/COS Photonics Asia, 2023, Beijing, China
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Abstract
An extended calibration procedure is required to collect sufficient data for establishing a stable and reliable subject-specific classifier before the user can use a brain-computer interface (BCI) system based on functional near-infrared spectroscopy (fNIRS). In addition, individual differences can lead to low generalization performance of the subject-specific classifier cross-subject. To address the above dilemma and improve the universality of the fNIRS-BCI system, we propose a revised Inception-ResNet (rIRN) model-based transfer learning (TL) to improve the cross-subject decoding accuracy of mental tasks. The TL-rIRN is a deep transfer learning model that combines an elaborated rIRN model for fNIRS signal classification with model-based transfer learning. The fNIRS data of eight participants are collected for the purpose of distinguishing between mental arithmetic and mental singing tasks. The leave-one-subject-out cross-validation method is used to evaluate the cross-subject decoding performance of TL-rIRN. The results show that TL-rIRN improves cross-subject decoding accuracy and effectively reduces model training time, calibration time, and computational resources.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yao Zhang and Feng Gao "A revised inception-ResNet model-based transfer learning for cross-subject decoding of fNIRS-BCI", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 127703I (27 November 2023); https://doi.org/10.1117/12.2689271
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KEYWORDS
Deep learning
Calibration
Machine learning
Brain-machine interfaces
Feature extraction
Data acquisition
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