This paper describes some new approaches for the feature extraction of SSVEP signal in EEG signal processing. One of them is Canonical Correlation Analysis (CCA) and another one is CWT along with ANN. Basically CCA is applied to analyze the frequency components of SSVEP in EEG. The essence of this method is to extract narrowband frequency components of SSVEP in EEG. The CWT offers a valuable tool for the analysis of signals as it provides precise location in terms of time of high frequency component. The selections of the mother wavelet having high correlation with the signal provide a more accurate timefrequency analysis. ANNs are considered to be good classifier due to their inherent features as robustness, adaptive learning, and generalization ability and self-organization capability.

 

Published In : IJCSN Journal Volume 4, Issue 3

Date of Publication : June 2015

Pages : 535 - 542

Figures :10

Tables : 02

Publication Link : Application of New Approaches for the Feature Extraction and Classification of EEG Signal Processing in Brain Research

 

 

 

Mamta Kumari : Department of Electronics & Telecommunication M.I.T. College of Engineering, University of Pune, India

Sunil B. Somani : Department of Electronics & Telecommunication M.I.T. College of Engineering, University of Pune, India

 

 

 

 

 

 

 

BCI- Brain Computer Interface

EEGElectroencephalography

SSVEP- Steady State Visual Evoked Potential

SNR- Signal-to-Noise Ratio

CCACanonical Correlation Analysis

In this project work the features of SSVEP signals are extracted successfully and classified them with good accuracy by both the CCA method and CWT along with ANN. After that we have generated control signals from classified data and developed a prototype using a microcontroller as an input datafor the desired output applications .It ismore user friendly and compatible for working in real time environment.

 

 

 

 

 

 

 

 

 

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