@inproceedings{0b4af17b1fc7468598cb218e19346307,
title = "Feature selection of EEG data with neuro-statistical method",
abstract = "Feature selection (FS) of high dimensional electroencephalographic (EEG) data helps to identify and diagnose the brain conditions easily. Features can be selected with different ways where canonical correlation analysis (CCA) is one of them which are a statistical method. We employed neural network (NN) with CCA for salient features extraction of EEG data, called Neural Canonical Correlation Analysis (NCCA), which exhibits better result than individual CCA or NN. A NN classifier is used to test the classification of the selected features. The NN classifier shows remarkable result in terms of recognition rate.",
keywords = "Clustering, Electroencephalogram (EEG), Feature selection (FS), Neural Canonical Correlation Analysis (NCCA), Neural network (NN)",
author = "Hossain, {Md Zakir} and Kabir, {Md Monirul} and Md Shahjahan",
year = "2014",
doi = "10.1109/EICT.2014.6777880",
language = "English",
isbn = "9781479922994",
series = "2013 International Conference on Electrical Information and Communication Technology, EICT 2013",
publisher = "IEEE Computer Society",
booktitle = "2013 International Conference on Electrical Information and Communication Technology, EICT 2013",
address = "United States",
note = "2013 International Conference on Electrical Information and Communication Technology, EICT 2013 ; Conference date: 13-02-2014 Through 15-02-2014",
}