Cognitive Activity Classification from EEG Signals with an Interval Type-2 Fuzzy System View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Shreyasi Datta , Anwesha Khasnobish , Amit Konar , D. N. Tibarewala

ABSTRACT

The present work attempts to classify Electroencephalogram (EEG) signals corresponding to three different cognitive activities using an Interval Type 2 Fuzzy System (IT2FS) classifier. This approach is used in order to account for the fact that EEG signals from the same person for the same stimulus have variations in different observations in the same as well as over different days of experiments. Adaptive Autoregressive Parameters, Hjorth Parameters, Hurst Exponents and Approximate Entropy features are extracted from the acquired EEG signals to obtain the maximum possible discrimination between different classes of activities. A maximum classification accuracy of 85.33 % on an average over all subjects and classes is obtained using IT2FS classifier on a combined feature space. The study is validated using Friedman’s statistical test with respect to four other classification algorithms. More... »

PAGES

235-247

References to SciGraph publications

Book

TITLE

Advancements of Medical Electronics

ISBN

978-81-322-2255-2
978-81-322-2256-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-81-322-2256-9_22

DOI

http://dx.doi.org/10.1007/978-81-322-2256-9_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016316172


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