Abstract:
EEG signals represent both the brain function and
also the status of the whole body, i.e. a simple action as blinking
the eyes introduces oscillation in the EEG records. The EEG is
a direct way to measure neural activities and it is important in
the area of biomedical research to understand and develop new
processing techniques. EEG signal pre-processing and postprocessing
methods include EEG signal modeling,
segmentation, filtering and de-noising, and EEG processing
methods which consist of two tasks, namely, feature
extraction/dimensionality reduction and classification. In this
paper, the performance analysis of Independent Component
Analysis (ICA) is considered as a dimensionality reduction
technique followed by Singular Value Decomposition (SVD) as
a Post Classifier for the Classification of Epilepsy Risk Levels
from EEG Signals. The analysis is done in terms of bench mark
parameters such as Performance Index (PI), Quality Values
(QV), Sensitivity, Specificity and Time Delay.
