Classification of EEG signals using the wavelet transform
Signal Processing
Galvanic skin response (GSR) as an index of cognitive load
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Using pen input features as indices of cognitive load
Proceedings of the 9th international conference on Multimodal interfaces
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Computational applications of nonextensive statistical mechanics
Journal of Computational and Applied Mathematics
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Towards automatic cognitive load measurement from speech analysis
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction design and usability
Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG
Computers in Biology and Medicine
Classification of working memory load using wavelet complexity features of EEG signals
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels.