Topological EEG-Based Functional Connectivity Analysis for Mental Workload State Recognition
Bibliographic record
Abstract
Mental workload (MWL) assessment is crucial in fatigue evaluation applications to avoid potential health problems or serious accidents. This article proposes an MWL recognition approach developed with a topological investigation of the electroencephalography (EEG)-based brain functional connectivity (FC) network. In this work, the graph-filtration-based features are extracted to reveal the brain state variations using the persistent homology technique from the topological data analysis (TDA) area. Three public open benchmark datasets are used to test and verify the recognition ability of the proposed method, which are developed with the MWL assessment experiments of Simultaneous Capacity (SIMKAP) test tasks, arithmetic calculations, and Multi-Attribute Task Battery II (MATB-II) MWL tasks. The experimental results show that the proposed topological FC network analysis scheme shows excellent distinguishing ability in brain state recognition, comparable to or better than the state-of-art results with similar settings. This work is the first investigation of EEG-based MWL evaluation with the persistent homology analysis of multivariate time series. The proposed topological features are effective and robust brain states’ indicators, providing an alternative feature in designing novel brain–computer interface systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".