# Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks

## TL;DR

This paper introduces a new deep learning model to assess cognitive workload from EEG data without relying on specific tasks.

## Contribution

The novel stacked graph attention convolutional networks (SGATCNs) model improves task-independent cognitive workload discrimination using EEG spatial information.

## Key findings

- The model uses differential entropy and power spectral density features across four frequency bands as node information.
- Phase-based connectivity measures and mutual information were evaluated to build functional brain networks.
- The framework achieved 65.11% average accuracy in recognizing cognitive workload across three task paradigms.

## Abstract

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios.