Deep-learning informed Kalman filtering for priori-free and real-time hemodynamics extraction in functional near-infrared spectroscopy

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

Automated summary

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.