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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