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Thomas Fischer^1,2, Eike Middell^1,2, Shakiba Moradi^1,2

¹Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany.

Journal of Neural Engineering

|June 5, 2026

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High-density diffuse optical tomography (HD-DOT) combined with short-separation (SS) regression and parcel-space features significantly improves brain signal decoding accuracy and generalization in functional Near-Infrared Spectroscopy (fNIRS) analysis.

Area of Science:

Neuroimaging
Biomedical Engineering
Signal Processing

Background:

Functional Near-Infrared Spectroscopy (fNIRS) signals are often sparse, limiting classification performance.
High-density diffuse optical tomography (HD-DOT) offers potential improvements but its impact on single-trial brain decoding is under-explored.
Existing methods lack systematic evaluation of probe density, physiological noise removal, and advanced feature representations.

Purpose of the Study:

To systematically evaluate the impact of HD-DOT, short-separation (SS) regression, and parcel-space features on single-trial fNIRS brain decoding accuracy.
To introduce a framework for augmenting resting-state fNIRS data with synthetic hemodynamic response functions (HRFs) for validation.
To quantify improvements in decoding accuracy and generalization across different datasets and probe configurations.

Keywords:

BCI GLM classification fNIRS generalization parcellation short-separation regression

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Main Methods:

Utilized three HD-fNIRS datasets, including resting-state data augmented with synthetic HRFs and motor task data.
Implemented GLM-based SS regression for physiological noise removal within cross-validation.
Compared channel-space and parcel-space features derived from HD-DOT image reconstructions.

Main Results:

HD-DOT configurations consistently improved classification accuracy and robustness.
SS correction enhanced within-subject decoding by 4% and cross-dataset transfer by over 10%.
Parcel-space features outperformed channel-space features, achieving 79% leave-one-subject-out decoding and 72% cross-dataset generalization with SS correction.

Conclusions:

HD-DOT, SS regression, and parcel-space representations collectively enhance fNIRS classification pipelines.
These integrated methods lead to more accurate, robust, and probe-independent brain decoding.
The open-source Cedalion framework facilitates implementation of these advanced techniques.