Xia, Yunjia;
Frijia, Elisabetta Maria;
Loureiro, Rui;
Cooper, Robert J;
Zhao, Hubin;
(2024)
An FPGA-based, multi-channel, real-time, motion artifact detection technique for fNIRS/DOT systems.
In:
2024 IEEE International Symposium on Circuits and Systems (ISCAS).
IEEE: Singapore.
(In press).
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Abstract
Functional Near-Infrared Spectroscopy (fNIRS) and its extension, Diffuse Optical Tomography (DOT), are emerging non-invasive neuroimaging techniques that measure brain activities by monitoring changes in blood oxygenation using near infrared light. However, motion artifacts from subject movements in fNIRS/DOT data could severely undermine data quality. Current solutions typically rely on offline methods executed on conventional computers in laboratories/hospitals, limiting real-time applications and flexibility in wider environments. To address these limitations, we present an FPGA-based multi-channel real-time motion artifact detection system. The proposed system, tested against an expert-annotated dataset, showcases encouraging overall performance, with a minimal delay of 2.75 ms across 12-channel raw fNIRS data, and boasts a sensitivity rate of 85.28% and accuracy of 87.06%. This efficiency is achieved using less than 10% of FPGA resources, underscoring that the proposed realtime processing system holds the potential to be scaled up to 3630 channels. These results indicate a promising avenue towards real-time motion artifact processing in large-size multichannel fNIRS/DOT data. Our design lays the groundwork for its application in areas including wearable real-time functional brain imaging, brain-computer interfaces, human-robot interaction, and surgical monitoring.
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