Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Occupational Health

Background:

• Advanced information and communication technology aims to simplify work, but inappropriate workload negatively impacts employee health and safety.
• Continuous monitoring of mental workload is crucial for optimizing work conditions and preventing adverse health effects.

Purpose of the Study:

• To develop and validate a novel method for continuous mental workload monitoring.
• To assess the relationship between cognitive task difficulty and electroencephalogram (EEG) spectral patterns.
• To establish an automated system for classifying mental workload levels.

Main Methods:

• Implementation of a cognitive task battery with varying complexity and difficulty levels.
• Collection of electroencephalogram (EEG) data, performance metrics, and NASA-TLX questionnaire responses from 54 participants.
• Analysis of EEG spectral power, specifically frontal theta and parietal alpha bands, in relation to task difficulty.

Main Results:

• Increased frontal theta band power and decreased parietal alpha band power correlated with higher task difficulty.
• Development of the Dual Frequency Head Maps (DFHM) method, utilizing support vector machines (SVMs) for classification of three workload levels.
• Empirical validation demonstrated the method's effectiveness across new subjects and tasks without classifier retraining.

Conclusions:

• The proposed DFHM method offers automatic, continuous mental workload classification, adaptable to new users and tasks without retraining.
• This technology can contribute to ensuring favorable working conditions, sustained performance, and improved employee health.
• The findings align with the cognitive demands placed on executive functions by varying task difficulties.