Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance

TL;DR

This study introduces two novel EEG-based methods for characterizing neural connectivity to predict cognitive performance on working memory tasks, demonstrating moderate classification accuracy in detecting recall failures.

Contribution

The paper presents two innovative approaches for analyzing functional neural connectivity from EEG data, comparing their effectiveness to traditional power-based features in predicting task performance.

Findings

Connectivity structure features achieved AUCs up to 0.63.

Graph variability features achieved AUCs around 0.58.

Power features achieved AUCs around 0.54.

Abstract

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.