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Cognitive Workload of Tugboat Captains in Realistic Scenarios: Adaptive Spatial Filtering for Transfer Between
Daniel Miklody1, Benjamin Blankertz1
1Electrical Engineering and Computer Science Department, Neurotechnology Group, Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.
Frontiers in Human Neuroscience
|February 14, 2022
Summary
A novel adaptive beamforming method improves Electroencephalography (EEG) cognitive workload estimation transferability between tasks. This approach effectively distinguishes neural signals from muscle artifacts, outperforming traditional methods in realistic, changing environments.
Area of Science:
- Neuroscience
- Cognitive Science
- Signal Processing
Background:
- Estimating cognitive workload using Electroencephalography (EEG) faces challenges due to signal non-stationarities and confounding muscle/eye activity.
- Transferring EEG findings from lab to real-world scenarios is difficult, often questioning if brain signals or artifacts drive classification.
Purpose of the Study:
- Investigate a novel adaptive beamforming spatial filtering approach for improved EEG cognitive workload estimation.
- Compare adaptive beamforming against no spatial filtering and Common Spatial Patterns (CSP) in realistic conditions and transfer settings.
Main Methods:
- Utilized a tugboat simulator with a maneuvering task and an auditory n-back task to induce workload variations.
- Applied adaptive beamforming, no filtering, and CSP for classification within and between these tasks.
- Analyzed classification performance and the origin of signal components (neural vs. artifact).
Main Results:
- Adaptive beamforming demonstrated superior performance in transferring workload classification between tasks (34-35% loss) compared to no filtering (45-53%) and CSP (45-53%).
- Within-condition classification showed comparable performance for adaptive beamforming (18-30%) and CSP (15-33%), while no filtering had the lowest loss (10-22%).
- Scalp pattern analysis indicated the adaptive beamforming approach primarily captured neural activity, which showed slight condition-specific pattern changes.
Conclusions:
- Adaptive beamforming is crucial for robust EEG cognitive workload estimation in dynamic, real-world scenarios, enabling successful transfer across different tasks.
- Traditional methods (no filtering, CSP) rely on condition-specific artifacts, limiting their transferability.
- The study highlights the need for adaptive spatial filtering to isolate neural signals for reliable workload assessment in changing environments.
