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Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature

Chutian Zhang^1,2, Hongjun Yang^2,3, Jiaxing Wang^2,3

¹Department of Engineering ScienceFaculty of Innovation EngineeringMacau University of Science and Technology Macau China.

IEEE Journal of Translational Engineering in Health and Medicine

|March 2, 2026

Summary

This summary is machine-generated.

This study introduces a new framework using functional near-infrared spectroscopy (fNIRS) to accurately detect mild cognitive impairment (MCI). The method integrates resting and task-state brain data, improving diagnostic accuracy for early dementia detection.

Keywords:

Feature dimensionality reduction fNIRS-based MCI diagnosis machine learning spatial filtering temporal feature selection

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Area of Science:

Neuroscience
Medical Technology
Machine Learning

Background:

Mild cognitive impairment (MCI) is a precursor to dementia, necessitating early detection for intervention.
Functional near-infrared spectroscopy (fNIRS) offers a non-invasive method for brain activity monitoring.
Current machine learning methods for MCI detection using fNIRS suffer from underutilized data and high dimensionality.

Purpose of the Study:

To develop a spatio-temporal feature engineering framework for improved MCI classification using fNIRS.
To address the limitations of underutilizing complementary resting-state and task-state fNIRS data.
To reduce feature dimensionality for more robust and generalizable MCI detection models.

Main Methods:

Independent component analysis (ICA) was used to derive spatial filters from resting-state fNIRS signals.
A universal population-level filter set was created by clustering subject-specific filters to isolate spatial features from task-state signals.
Temporal feature selection identified discriminative task-evoked time points, reducing dimensionality for MCI detection.

Main Results:

The framework achieved 90.91% accuracy in classifying cognitively normal individuals versus those with MCI.
A significant feature dimensionality reduction of 91.07% was attained.
Analysis revealed universal spatial filters linked to MCI biomarkers and critical temporal decision points during cognitive tasks.

Conclusions:

The proposed framework effectively integrates resting-state and task-state fNIRS data for enhanced MCI detection.
Dimensionality reduction leads to higher accuracy and interpretability in fNIRS-based MCI classification.
This advancement holds potential for generalizable MCI detection and efficient clinical data expansion.