# Cognitive Domains Classification Using fNIRS-EEG

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $198,750

## Abstract

Abstract. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that uses low-levels of
light (650-900 nm) to noninvasively measure changes in cerebral blood volume and oxygenation. Meanwhile,
electroencephalography (EEG) measures the neural oscillatory activity which can be divided into various
rhythms frequency bands. We propose a paradigm shifting analysis approach where instead of using EEG or
fNIRS to estimate and test the de novo brain activation pattern for a specific task, we utilize our existing
knowledge of common patterns of brain activity from similar tasks done in fMRI to create a set of testable [null]
hypotheses; namely, “is the pattern of fNIRS/EEG measured [not] consistent with what is expected from (e.g.)
the general category of working memory tasks?”
 Our innovative proposal is to map brain signals onto the “cognitive domains” underlying complex brain
functions rather than task themselves. Specifically, we purpose to use a projection (reparameterization) of
brain signals using spatial maps defined by the six key cognitive domains defined by the DSM-V; language,
perceptual-motor, executive function, complex attention, social cognition, and learning and memory. These
maps are generated from meta-analysis of hundreds of existing fMRI studies pooled across all tasks related to
the cognitive domain as a keyword allowing us to develop model training from a large repository of varied
tasks. This reparameterization and compression of the brain signals into this low dimensional space provides a
task-agnostic, statistically testable, and interpretable signal, which can be examined in real-time. The main
aims of this study are:
 · Aim 1. Develop and validate fNIRS-EEG features in cognitive domains.
 · Aim 2. Test-retest reliability of fNIRS-EEG features.
 · Aim 3. Quantitative sensitivity-specificity report of the prediction model.
 · Aim 4. Implementation real-time fNIRS-EEG for cognitive domain classification.

## Key facts

- **NIH application ID:** 10900626
- **Project number:** 5R21EB033954-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Hendrik Santosa
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $198,750
- **Award type:** 5
- **Project period:** 2023-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10900626

## Citation

> US National Institutes of Health, RePORTER application 10900626, Cognitive Domains Classification Using fNIRS-EEG (5R21EB033954-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10900626. Licensed CC0.

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