Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural networ...

Full description

Bibliographic Details
Main Authors: Maja eStikic, Chris eBerka, Daniel eLevendowski, Roberto Felipe Rubio, Veasna eTan, Stephanie eKorszen, Douglas eBarba, David eWurzer
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-11-01
Series:Frontiers in Neuroscience
Subjects:
Electroencephalography (EEG)
unsupervised learning
self-organizing map
cognitive state
electrocardiography (ECG)
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00342/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00342/full

Similar Items