Feature Extraction and Classification of EEG Signals Using Neural Network Based Techniques

Abstract

EEG stands for Electroencephalogram. EEG is used to record signals from brain; signals are
recorded from the scalp or cortex of brain. EEG used for both clinically purpose as well as
for scientifically purpose. Hence measurement of EEG signals plays an important role in
mind/brain studies. Reorganization of EEG signals from brain is one of the most overriding
approaches to extract the data/knowledge from mind/brain dynamics. Analyzing Electrical
activity of brain through EEG provide medical science to examine different brain diseases.
Electrical activity of brain can easily be classified as normal brain waves or abnormal brain
waves. Normal brain waves used to study various states of mind where as abnormal brain
waves used to indicate medical problems. Classification of EEG signals play important role
in medical science, some important applications for EEG wave classification are diagnosis of
sleep disorders and construction of BCI to assist disabled person.
Reorganization of EEG signals from brain is one of the most overriding approaches to
extract the data/knowledge from mind/brain dynamics. Analyzing Electrical activity of brain
through EEG provide medical science to examine different brain diseases.