High performance EEG signal classification using classifiability and the Twin SVM

Volume: 30, Pages: 305 - 318
Published: May 1, 2015
Abstract
Classification of Electroencephalogram (EEG) data for imagined motor movements has been a challenge in the design and development of Brain Computer Interfaces (BCIs). There are two principle challenges. The first is the variability in the recorded EEG data, which manifests across trials as well as across individuals. Consequently, features that are more discriminative need to be identified before any pattern recognition technique can be applied....
Paper Details
Title
High performance EEG signal classification using classifiability and the Twin SVM
Published Date
May 1, 2015
Volume
30
Pages
305 - 318
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