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  Wavelet Based Classification of Finger Movements Using EEG Signals  
  Authors : R.Shantha Selva Kumari; P.Induja
  Cite as:

 

Brain-computer interfaces (BCIs) have been examined in the field of bio-medical engineering. This brain-computer interface method is very useful for the people who are suffered by some nervous disorder to control or operate the external devices. EEG dataset are acquired and these signals are processed for identifying the brain thoughts to control the device. Here we proposed the method for the classification of the finger movements using EEG signals which are used in the application of artificial upper limb. This method includes pre processing, feature extraction and feature classification. Pre processing includes the removal of artifacts in the EEG signals due to some noises like eye blinking, etc. Discrete Wavelet Transform is used for the feature extraction. Features in both time domain and frequency domain are evaluated on the various EEG Signals. Based on the features extracted from various EEG signals, they are classified for the different finger movements using SVM classifier. The accuracy of 96.67% has been achieved for the proposed finger movement’s classification.

 

Published In : IJCSN Journal Volume 4, Issue 6

Date of Publication : December 2015

Pages : 873- 886

Figures :20

Tables : 05

Publication Link : Wavelet Based Classification of Finger Movements Using EEG Signals

 

 

 

R. Shantha Selva Kumari : received her BE degree in Electronics and Communication Engineering from Bharathiyar University, in 1987 and MS degree in Electronics and Control from Birla Institute of Technology, Pilani, in 1994. She completed her PhD degree in Bio-signal processing in 2008 from Manonmanium Sundaranar University, Tirunelveli. She has 28 years of teaching experience and is currently working as Professor and Heading the Department of Electronics and Communication Engineering at Mepco Schlenk Engineering College, India. Her current research interest includes signal processing, wavelets and its applications, neural networks. She is a life member in ISTE, FIETE and CSI.

P.Induja : received her B.E degree in Electronics and Communication Engineering from Mepco Schlenk Engineering College, in 2014 and doing M.E degree in Communication Systems at Mepco Schlenk Engineering College, Sivakasi.

 

 

 

 

 

 

 

Brain Signals

Electroencephalogram (EEG)

Discrete Wavelet Transform

Support Vector Machine (SVM)

The proposed work is to analyse classification of the finger movements using EEG signals in particular Left and Right hand fingers open and close are focused. Alpha and Beta bands of the signals are extracted using the Discrete Wavelet Transform. Four features in time domain and two features in frequency domain are extracted. These six features can be fed to the classifier for further classification of different finger movements which are used in the application of artificial upper limb. The accuracy of 96.67% is achieved for the wavelet based classification of finger movements using EEG signals.

 

 

 

 

 

 

 

 

 

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