Independent component analysis: algorithms and applications
Neural Networks
BiosignalsStudio: a flexible framework for biosignal capturing and processing
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Online workload recognition from EEG data during cognitive tests and human-machine interaction
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Multimodal Recognition of Cognitive Workload for Multitasking in the Car
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
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This paper presents an online multimodal person independent workload classification system using blood volume pressure, respiration measures, electrodermal activity and electroencephalography. For each modality a classifier based on linear discriminant analysis is trained. The classification results obtained on short data frames are fused using weighted majority voting. The system was trained and evaluated on a large training corpus of 152 participants, exposed to controlled and uncontrolled scenarios for inducing workload, including a driving task conducted in a realistic driving simulator. Using person dependent feature space normalization, we achieve a classification accuracy of up to 94% for discrimination of relaxed state vs. high workload.