# A Cross-Subject Band-Power Complexity Metric for Detecting Mental Fatigue Through EEG

## TL;DR

A new EEG metric called ST-SODE detects mental fatigue without needing subject-specific calibration, making it suitable for real-world applications like driving and healthcare.

## Contribution

ST-SODE is a novel EEG-based metric that improves cross-subject and cross-domain fatigue detection by suppressing background brain rhythm interference.

## Key findings

- ST-SODE achieves a correlation coefficient of 0.56 on the SEED-VIG dataset, outperforming differential entropy.
- ST-SODE reaches 93.75% binary classification accuracy on a vigilance dataset based on the N-Back task.
- ST-SODE reduces the need for calibration and enables lightweight cross-subject deployment for fatigue monitoring.

## Abstract

What are the main findings?
The proposed Short-Term Second-Order Differential Entropy (ST-SODE) can capture fatigue from short-term band-power dynamics.ST-SODE improves the robustness of cross-domain EEG fatigue detection.

The proposed Short-Term Second-Order Differential Entropy (ST-SODE) can capture fatigue from short-term band-power dynamics.

ST-SODE improves the robustness of cross-domain EEG fatigue detection.

What are the implications of the main findings?
ST-SODE reduces calibration burden for real-world fatigue monitoring.ST-SODE enables lightweight cross-subject deployment.

ST-SODE reduces calibration burden for real-world fatigue monitoring.

ST-SODE enables lightweight cross-subject deployment.

Background/Objectives: Electroencephalography (EEG) is a promising modality for fatigue detection because it directly reflects neural states; however, it is hindered by the need for subject-specific calibration and its reliance on unstable labeling. Moreover, classical EEG features are sensitive to intrinsic brain rhythm variations, causing pronounced domain shifts that degrade performance across sessions and subjects. Methods: Motivated by the biological fatigue rebound mechanism, we propose a robust cross-subject metric which we name Short-Term Second-Order Differential Entropy (ST-SODE). ST-SODE effectively suppresses the interference of background brain rhythms, enhancing robustness to cross-domain drift; consequently, its one-dimensional output can provide an indication of fatigue states without additional model training. Results: ST-SODE is validated on the public driving fatigue regression dataset SEED-VIG and on a private Vigilance classification dataset based on the N-Back task. ST-SODE achieves a correlation coefficient of 0.56 on SEED-VIG dataset (vs. 0.4 for differential entropy, DE) and a binary classification accuracy of 93.75% on the Vigilance dataset, outperforming other EEG-based fatigue metrics. Conclusions: ST-SODE offers a reliable solution for deployment in fields such as driving, manufacturing, and healthcare, where it could reduce safety incidents caused by fatigue.