A revised inception-ResNet model-based transfer learning for cross-subject decoding of fNIRS-BCI

Yao Zhang; Feng Gao

doi:10.1117/12.2689271

27 November 2023 A revised inception-ResNet model-based transfer learning for cross-subject decoding of fNIRS-BCI

Yao Zhang, Feng Gao

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

Conference Poster

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.

Citation Download Citation

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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