Investigate the Features for Analysis of EEG Signals Using Multivariate Empirical Mode Decomposition
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 9
Abstract
BCI (Brain Computer Interface) is collaboration between neural activity of the brain and an external device. These are the control and communication systems which converts human brain signals into commands and messages in order to control application such as moving a pointer on a computer, typing letters using a virtual keyboard. The neural activity of the brain can be interpreted by EEG signal. In this paper, the performance of feed forward backpropagation classifier for classification of three different mental tasks such as baseline, mental arithmetic and letter composing were investigated. Multivariate Empirical Mode Decomposition (MEMD) was used for features extraction of the raw EEG signal. The new features have been investigated for three mental tasks for classifying a small set of non-motor cognitive task. The discriminatory power of features has been investigated using paired t-test. The neural network were trained and tested for all three mental tasks. The classification accuracy during combination of three mental tasks was found near about 80% to 90%.
Authors and Affiliations
Parul Mangal
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