Measurement of Cognitive Workload by Use of Combined Methods Including Brain-Computer Interfaces

Firat Özkan, N.; Kahya, Emin · 2020 · Crossref

DOI: 10.54941/ahfe100358

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of accurately measuring cognitive workload, a critical factor in human factors and ergonomics for reducing human error and preventing accidents. The authors argue that existing measurement methods—subjective, behavioral, and physiological—each possess distinct limitations, such as low objectivity or sensitivity to environmental conditions. Consequently, the study explores the potential of combining these traditional methods with Brain-Computer Interface (BCI) technology to create more reliable, hybrid assessment tools. The motivation stems from the need to monitor cognitive states, particularly in high-risk environments, where understanding the relationship between task demands and individual capacity is essential for system safety and performance. The authors review established cognitive load measurement techniques, including subjective tools like NASA-TLX, behavioral primary/secondary task performance metrics, and physiological indicators such as heart rate variability, galvanic skin response, and eye-tracking data. They specifically examine BCI technologies that utilize Electroencephalography (EEG) to detect specific brain potentials: P300 (associated with decision-making and attention), Steady State Evoked Potentials (SSVEP), and motor imagery. The paper proposes a combined methodology using a G.Tec BCI system integrated with sensors for physiological data (HRV, GSR, respiration) and remote eye-tracking. In this proposed design, participants perform standardized BCI tasks—such as typing via a letter matrix, controlling devices via SSVEP, or moving cursors via motor imagery—while concurrent physical and behavioral data are recorded. This approach aims to eliminate performance variations caused by individual skill differences, focusing instead on pure cognitive attention and fatigue levels. The study finds that while no single method provides a definitive measure of cognitive load, combining methods increases estimation reliability. The authors highlight that BCI tasks offer advantages over traditional secondary tasks because they require high levels of attention and are less dependent on personal qualifications or interests. The proposed combined method allows for the simultaneous tracing of key physiological indicators and EEG data, enabling a multi-aspect analysis of cognitive state. However, the authors note limitations, including the requirement for controlled, silent environments and the unsuitability of BCI tasks as secondary tasks in real-world scenarios due to their high attention demands. The method is deemed more appropriate for estimating mental fatigue or monitoring cognitive load before or after critical tasks rather than during them. The significance of this work lies in its contribution to the development of adaptive systems capable of monitoring user mental workload to reduce error risks. By integrating BCI with traditional physiological and subjective measures, the authors propose a pathway toward more objective and sensitive cognitive load assessment. The paper concludes that future research should focus on experimental data collection in high-risk sectors and across different age groups to validate these combined methods and develop new risk analysis frameworks that incorporate cognitive aspects. This approach represents an initial step toward leveraging advanced neurotechnology for improved human-machine system design and safety.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• workload measurement
• neuro workload indices
• mental demand

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data, self report data