Canonical correlation analysis with neural network for inter subject variability realization of EEG data

Author

Hossain, Md Zakir ; Rabin, Md Jubayer Alam ; Uddin, A. F. M. Nokib ; Shahjahan, Md

Author_Institution

Khulna Univ. of Eng. & Technol., Khulna, Bangladesh

fYear

2013

fDate

17-18 May 2013

Firstpage

1

Lastpage

5

Abstract

The detection of brain condition under different subjects is utmost important and it is a challenging task. EEG signals are such data that need to carefully analyze when it consists of series of different subjects. This paper explores the application of canonical correlation analysis with artificial neural networks for EEG data sets with different subjects and reference. We demonstrate the network´s capabilities on EEG data to determine their subject to subject dependency in terms of correlation and then compare its effectiveness with that of a sine-cosine reference signals.

Keywords

correlation methods; electroencephalography; medical signal processing; neural nets; EEG dataset; EEG signals; artificial neural networks; brain condition detection; canonical correlation analysis; intersubject variability realization; sine-cosine reference signals; Artificial neural networks; Biological neural networks; Correlation; Electroencephalography; Steady-state; Time series analysis; Visualization; Artificial Neural Networks (ANN); Canonical Correlation Analysis (CCA); Electroencephalogram (EEG); Variability;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics, Electronics & Vision (ICIEV), 2013 International Conference on

Conference_Location

Dhaka

Print_ISBN

978-1-4799-0397-9

Type

conf

DOI

10.1109/ICIEV.2013.6572711

Filename

6572711