A potential rapid detection of cognitive status in the brain: an fNIRS study

Qi, Tianrui; Wang, Xiaodan; Zheng, Yiyuan; Fu, Shan; Lu, Yanyu · 2024 · Crossref

DOI: 10.54941/ahfe1004753

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Summary

This study addresses the need for rapid, objective assessment of pilot cognitive states in aviation, where human error remains a primary cause of accidents. While functional near-infrared spectroscopy (fNIRS) is suitable for cockpit environments due to its portability and resistance to motion artifacts, traditional methods often rely on time windows exceeding five seconds, causing delays that hinder the detection of rapid state transitions during emergencies. The research aims to develop a faster detection method by extracting local connectivity (LC) features from fNIRS data, specifically utilizing correlation coefficients of adjacent channels to identify early-stage brain activation patterns. The experimental design involved nine healthy, right-handed participants (aged 21–24) performing a visual search task. Participants were required to locate a verbally announced number within a set of 25 random numbers displayed on a screen. fNIRS data was acquired using a 24-channel system (Artinis Brite24) placed over the frontal lobe, with 48 measurement channels and a sampling rate of 50 Hz. The protocol included resting states and task blocks, with data preprocessed by removing physiological noise via a combination of notch and bandpass filters. The analysis focused on oxygenated hemoglobin (HbO) signals. Pearson correlation matrices were calculated using a sliding window of 1.5 seconds with a 0.1-second step. Local connectivity was defined as the squared sum of correlation coefficients between adjacent channels for each node in the network. Activation was determined by comparing task-state LC to a resting-state baseline, with a threshold difference of 2.5 used to identify significant changes. Results indicated that LC features reflected changes in brain activation patterns as early as 0.5 to 2.5 seconds after stimulus onset, significantly faster than traditional single-channel mean features which typically require 8–10 seconds. Statistical analysis using paired t-tests with False Discovery Rate (FDR) correction identified nodes R1, T5, and R8 in the left and central prefrontal regions as showing significant increases in LC during the task. These regions correspond to the left ventrolateral and dorsolateral prefrontal cortices, areas associated with search, memory maintenance, and judgment. The activation ratio for these nodes reached 70% within three seconds of the task. The findings confirm that bilateral dorsolateral and left ventrolateral prefrontal activation occurs rapidly during cognitive-executive tasks. The significance of this work lies in the development of a novel LC feature that enables the rapid identification of brain task states. By detecting cognitive changes within the first two seconds of a task, this method reduces judgment delays compared to existing techniques, thereby enhancing the potential for real-time monitoring of pilot workload and safety in complex cockpit environments. The study demonstrates that multi-channel connectivity features provide a more sensitive and temporally precise indicator of cognitive status than traditional univariate approaches.

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• neuro workload indices