Xia, Yunjia;
Chen, Jianan;
Li, Jinchen;
Gong, Tingchen;
Vidal-Rosas, Ernesto E;
Loureiro, Rui;
Cooper, Robert J;
(2025)
A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.
IEEE Transactions on Neural Systems and Rehabilitation Engineering
, 33
pp. 1220-1230.
10.1109/TNSRE.2025.3553794.
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Abstract
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.
| Type: | Article |
|---|---|
| Title: | A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TNSRE.2025.3553794 |
| Publisher version: | https://doi.org/10.1109/tnsre.2025.3553794 |
| Language: | English |
| Additional information: | © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Rehabilitation, Engineering, Real-time systems, US Department of Transportation, Hemodynamics, Functional near-infrared spectroscopy, Optical imaging, Three-dimensional displays, Feedback amplifiers, Jacobian matrices, Image reconstruction, Electroencephalography, Functional near-infrared spectroscopy (fNIRS), diffuse optical tomography (DOT), brain-computer interface (BCI), neurofeedback (NFB), motion artifacts, deep learning, real-time processing, NEAR-INFRARED SPECTROSCOPY |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Ortho and MSK Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10219919 |
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