A Synchronous EEG-fNIRS BCI: A Proof-of-Concept for Multimodal Avalanche Analysis of Motor Cognition in Older Adults

TL;DR

This study presents a novel multimodal EEG-fNIRS framework combined with neuronal avalanche analysis to detect early network dysfunction in Alzheimer's disease, demonstrating technical feasibility and potential diagnostic value.

Contribution

It introduces a new synchronized multimodal approach integrating EEG and fNIRS with neuronal avalanche analysis for early Alzheimer's detection.

Findings

Reduced neural contrast between motor states in MCI

Increased variability in network organization in MCI

Framework validated with preliminary pilot data

Abstract

This proof-of-concept study introduces a novel multimodal framework combining synchronized EEG-fNIRS modalities with neuronal avalanche analysis to identify early network dysfunction in Alzheimer's disease. The approach leverages complementary neural signals to examine motor network dynamics during execution and imagery tasks within an interactive task environment. Preliminary analysis of a small pilot cohort (N=4 subjects, including one with Mild Cognitive Impairment) validated the technical feasibility of the multimodal framework and revealed observable condition-dependent patterns in network organization. Two primary observations emerged: a reduced neural contrast between motor execution and imagery states, and increased trial-to-trial variability in network organization in the MCI participant. These initial results successfully validate the technical pipeline and provide hypothesis-generating observations for future statistically powered studies. The convergence of findings across modalities suggests that multimodal assessment of network flexibility may help detect functional changes in early Alzheimer's continuum, supporting the future development of this BCI-inspired framework into an engaging diagnostic tool.