Investigating the relationship between EEG features and N-back task difficulty levels with NASA-TLX scores among undergraduate students

Harputlu Aksu, Şeniz; Çakıt, Erman; Dağdeviren, Metin · 2023 · Crossref

DOI: 10.54941/ahfe1002828

archive: archived pipeline: cataloged verified

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Summary

This study investigates the relationship between task difficulty, objective performance metrics, subjective mental workload assessments, and electroencephalogram (EEG) features. Motivated by the need for reliable mental workload measurement in human-machine interactions, the research aims to validate whether predetermined difficulty levels in N-back memory tasks effectively manipulate mental workload and to identify specific EEG band power variations associated with these levels. The study specifically examines how theta, alpha, and beta power change across different brain regions as task complexity increases. The experimental design involved 25 healthy undergraduate students who performed N-back memory tests at four difficulty levels (0-back to 3-back). Mental workload was assessed subjectively using the NASA-Task Load Index (TLX), while objective measures included response latency and accuracy rates. EEG data were recorded using a 14-channel EMOTIV EPOC X device. Due to signal quality constraints, the final EEG analysis included 15 participants. Data preprocessing involved filtering, line noise removal, and Independent Component Analysis to eliminate ocular and muscular artifacts. Statistical analyses, including Spearman correlation and Kruskal-Wallis tests, were conducted to evaluate relationships between task difficulty, NASA-TLX scores, performance variables, and 84 EEG features derived from five frequency bands. The results demonstrated a significant positive correlation between task difficulty and both perceived workload (weighted NASA-TLX score) and response latency. As difficulty increased, accuracy rates decreased, and participants reported higher mental demand, effort, and temporal demand. EEG analysis revealed distinct neural patterns associated with increased workload: theta power significantly increased in prefrontal, frontal, and central regions, while alpha power decreased in temporal, parietal, and occipital regions. Additionally, low beta power decreased across almost all brain regions. Specifically, theta power in channels AF3, AF4, F7, F8, and FC5 showed the highest correlation with task difficulty. Statistically significant differences were found for 49 EEG features across difficulty levels, with 34 showing a smooth monotonic change. Men reported higher NASA-TLX scores than women on difficult tasks, though no gender differences were observed in performance metrics. The study concludes that pre-determined N-back difficulty levels successfully manipulate mental workload, validating their use in future classification models. The findings confirm that EEG signals, particularly theta and alpha activities in specific brain regions, are sensitive indicators of mental workload. The strong correlations between subjective assessments, performance data, and EEG features support the integration of these multi-modal techniques for robust mental workload analysis in ergonomic and human factors research.

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Topics

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• neuro workload indices
• workload measurement
• mental demand

Information type

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• Empirical Findings: self report data, physiological data