Analysis of Singular Value Decomposition as a Dimensionality Reduction Technique and Sparse Representation Classifier as a Post Classifier for the Classification of Epilepsy Risk Levels from EEG Signals

The main aim of this paper is to perform the analysis of Singular Value Decomposition (SVD) as a Dimensionality Reduction technique and Sparse Representation Classifier (SRC) as a Post Classifier for the Classification of Epilepsy Risk levels from Electroencephalography signals. The data acquisition of the EEG signals is performed initially. Then SVD is applied here as a dimensionality reduction technique and then Sparse Representation Classifier is used for the Classification of Epilepsy Risk levels from EEG signals. The performance of the SVD with the SRC are compared based on the parameters such as Performance Index (PI) and Quality Value (QV).