Real-time mental workload measurement for four break configurations in cognitive tasks using the EEG-based workload index (EWI)
DOI: 10.21203/rs.3.rs-4442355/v1
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Summary
This study addresses the increasing mental workload (MWL) in complex high-tech manufacturing environments, where elevated cognitive demands are linked to poor performance, errors, and health issues. The primary objective was to validate the EEG-based Workload Index (EWI) as a real-time physiological measure of MWL during cognitive tasks, specifically examining how different break configurations affect workload. The research gap identified was the lack of studies applying the EWI model to cognitive tasks executed via a graphical user interface (GUI) with synchronized EEG data. The experimental design involved 45 healthy university students (average age 20.49 years) randomly assigned to four groups with distinct break protocols: Group 1 (control) performed a 60-minute continuous task; Group 2 completed two 30-minute blocks separated by a 2-minute break; Group 3 performed three 20-minute blocks with 2-minute breaks between the first two; and Group 4 executed two 40-minute blocks separated by a 5-minute break. Participants performed a Dual N-back cognitive task involving memorization and arithmetic operations via a MATLAB-based GUI. EEG data was collected in real-time using an OpenBCI Cyton V3-32 card with eight electrodes placed according to the 10-20 standard. Data synchronization between the cognitive stimuli and EEG signals was achieved using the Lab Streaming Layer (LSL) system. Post-processing involved filtering signals (0.5–42 Hz) using EEGLAB in MATLAB, calculating Power Spectral Density (PSD) for delta, theta, alpha, beta, and gamma bands, and computing the EWI using the ratio of (beta + gamma power) to (alpha + theta power). The results demonstrated that the EWI model effectively tracked MWL fluctuations corresponding to task difficulty and rest periods. The study successfully validated the EWI as an objective index for real-time MWL monitoring. The integration of LSL allowed for precise temporal alignment of EEG data with specific cognitive events (memorization and arithmetic operations), enabling the calculation of EWI for each interval of brain activity. The GUI-based approach provided a novel method for inducing and measuring MWL without the interference of subjective self-reporting biases. The significance of this work lies in the development of a low-cost, open-source hardware and software framework for real-time cognitive monitoring. By validating the EWI in a controlled experimental setting with varied break schedules, the study provides a foundation for implementing automated human-machine systems that can detect operator fatigue or error risk in real-time. This approach has direct implications for improving worker safety and productivity in high-tech industries by enabling dynamic adjustments to task demands or break schedules based on physiological feedback rather than fixed schedules.
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 | — | — | 4 | 2026-08-23 |
| 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 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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Information type
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- Empirical Findings: physiological data
- Methodological Resource: metric or index