Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Febi Elizabath Jacob | Aswathy Madhu

Volume 02 Issue 11  Year 2015

ISSN no: 2348-3121

Page no: 38-43

Abstract This  paper  introduces  an application of artificial neural  network  (ANN) for the classification  of EEG  signal in the  context  of hand  movement  parameters. Brain Computer Interface (BCI) is a communication system which enables the user to control special computer applications by using his or her thoughts.  To achieve this, noninvasive electroencephalography (EEG) based BCI is used. An experiment was conducted to collect EEG data from subjects while they executed hand movements at two different speeds, namely fast and slow movements.  Depending  on BCI,  particular preprocessing and feature  extraction process  are  applied  to the  EEG  sample  of certain  length, which is then possible to detect the task-specific EEG signals or patterns from the EEG samples with a certain level of accuracy. The discriminative movement related features from the data sets were obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm.  From these features, fast and slow hand movements were classified using ANN. The classification performances are analyzed by considering the number of input features, hidden neurons, training algorithms, connection between network   outputs   and targets, and mean square error. The outcomes of the analysis show that the best design of Levenberg Marquardt based neural network classifier can perform well with an average classification success rate of 97.50%.

Keywords Brain Computer Interface (BCI); Common Spatial Pattern (CSP); Discrete Wavelet Transform (DWT); Electroencephalography (EEG); movement related parameter; Artificial Neural Network (ANN).


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