NDLI: Unsupervised approach for measurement of cognitive load using EEG signals

Content Provider	IEEE Xplore Digital Library
Author	Das, D. Chatterjee, D. Sinha, A.
Copyright Year	2013
Description	Author affiliation: Innovation Labs., Tata Consultancy Services Ltd., Bangalore, India (Das, D.; Chatterjee, D.; Sinha, A.)
Abstract	Individuals exhibit different levels of cognitive load for a given mental task. Measurement of cognitive load can enable real-time personalized content generation for distant learning, usability testing of applications on mobile devices and other areas related to human interactions. Electroencephalogram (EEG) signals can be used to analyze the brain-signals and measure the cognitive load. We have used a low cost and commercially available neuro-headset as the EEG device. A universal model, generated by supervised learning algorithms, for different levels of cognitive load cannot work for all individuals due to the issue of normalization. In this paper, we propose an unsupervised approach for measuring the level of cognitive load on an individual for a given stimulus. Results indicate that the unsupervised approach is comparable and sometimes better than supervised (e.g. support vector machine) method. Further, in the unsupervised domain, the Component based Fuzzy c-Means (CFCM) outperforms the traditional Fuzzy c-Means (FCM) in terms of the measurement accuracy of the cognitive load.
Sponsorship	IEEE Comput. Soc.
Starting Page	1
Ending Page	6
File Size	329177
Page Count	6
File Format	PDF
ISBN	9781479931637
DOI	10.1109/BIBE.2013.6701686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-10
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Support vector machines Classification algorithms Training Layout Standards Cognition
Content Type	Text
Resource Type	Article

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