Principal Component Analysis as a Dimensionality Reduction Technique and Sparse Representation Classifier as a Post Classifier for the Classification of Epilepsy Risk Levels from EEG Signals
نویسندگان
چکیده
The main aim of this paper is to perform the analysis of Principal Component Analysis (PCA) 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 PCA 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 PCA with the SRC are compared based on the parameters such as Performance Index (PI) and Quality Value (QV).
منابع مشابه
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