Mental Workload Estimation from EEG Signals Using Machine Learning Algorithms
DOI: 10.1007/978-3-319-91122-9_23
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Summary
This study addresses the challenge of objectively estimating mental workload (MWL) in high-stakes environments such as defense and aviation, where traditional subjective or performance-based measures are often inadequate due to bias and inter-personal variability. The authors propose a machine learning-based approach using electroencephalogram (EEG) signals to automatically classify MWL levels. The research aims to validate the feasibility of low-cost, wireless EEG devices for real-time cognitive assessment and to determine the impact of feature engineering and channel selection on classification accuracy. The experimental protocol involved ten healthy participants who performed five distinct cognitive tasks designed to induce varying levels of mental load: idle, 1-back, 2-back, dual 1-back, and dual 2-back. Data were collected using a wireless Emotiv Epoc+ EEG device with 14 channels sampled at 128 Hz. The raw signals underwent preprocessing to remove artifacts using the FORCe algorithm. The researchers then applied Mutual Information for channel selection, identifying frontal lobe channels as most relevant to cognitive workload. Feature extraction involved calculating statistical, derivative, interval, Hjorth, frequency-domain, and wavelet features from 3-second epochs. These features were optimized using the Maximum Relevance Minimum Redundancy (mRMR) algorithm to reduce dimensionality. Finally, seven supervised machine learning classifiers—including Random Forest, Support Vector Machines, and K-Nearest Neighbors—were trained and tested on the data to classify the five workload levels. The results demonstrated that machine learning algorithms could effectively distinguish between different MWL levels. Spectrogram analysis revealed that theta and alpha bands were dominant across tasks, with beta band activity increasing during high-load dual 2-back tasks. Classification accuracy improved significantly after applying channel selection and feature optimization. The Random Forest classifier consistently outperformed other models. In two-class classification scenarios, Random Forest achieved an average accuracy of 92.26% without optimization and 92.93% with optimization, peaking at 99.19% for specific class pairs. For the most complex five-class classification, the optimized Random Forest model achieved an accuracy of 84.61%, compared to 80.22% without optimization. Other classifiers, such as Linear Discriminant Analysis and Multi-Layer Perceptron, also showed improved performance post-optimization, though they remained less accurate than Random Forest. The study concludes that wireless EEG combined with optimized feature sets and robust machine learning algorithms provides a viable, objective method for mental workload estimation. The findings suggest that reducing data dimensionality through strategic channel selection and feature optimization enhances classifier performance, making this approach suitable for real-time applications in human-machine interaction and critical operations. This work supports the integration of non-invasive brain-computer interfaces into systems requiring continuous monitoring of operator cognitive states.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 2 | 2026-08-23 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: physiological data