Journal
BIOMEDICAL OPTICS EXPRESS
Volume 13, Issue 9, Pages 4787-4801Publisher
Optica Publishing Group
DOI: 10.1364/BOE.467943
Keywords
-
Funding
- National Natural Science Foundation of China [61575140, 62075156, 81871393, 81971656]
- Tianjin Science and Technology Committee [18JCYBJC29400]
Ask authors/readers for more resources
Separation of physiological interferences and neural hemodynamics is crucial for the implementation of functional near-infrared spectroscopy (fNIRS). Existing solutions rely on priori information, hindering real-time application. To overcome this, a novel priori-free scheme combining deep-learning-based interference characterization and Kalman filtering is proposed for real-time interference suppression and activation extraction.
Separation of the physiological interferences and the neural hemodynamics has been a vitally important task in the realistic implementation of functional near-infrared spectroscopy (fNIRS). Although many efforts have been devoted, the established solutions to this issue additionally rely on priori information on the interferences and activation responses, such as time-frequency characteristics and spatial patterns, etc., also hindering the realization of real-time. To tackle the adversity, we herein propose a novel priori-free scheme for real-time physiological interference suppression. This method combines the robustness of deep-leaning -based interference characterization and adaptivity of Kalman filtering: a long short-term memory (LSTM) network is trained with the time-courses of the absorption perturbation baseline for interferences profiling, and successively, a Kalman filtering process is applied with reference to the noise prediction for real-time activation extraction. The proposed method is validated using both simulated dynamic data and in-vivo experiments, showing the comprehensively improved performance and promisingly appended superiority achieved in the purely data-driven way.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available