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

• Main Authors: Zhang, Yao, Gao, Feng
• Format: Conference Proceeding
• Language: English
• Published: SPIE 27.11.2023
• ISBN: 151066789X; 9781510667891
• ISSN: 0277-786X
• DOI: 10.1117/12.2689271

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.