Significance of Independent Component Analysis (ICA) for Epileptic Seizure Detection Using EEG Signals

摘要

Electroencephalography (EEG) is a measurement tool to measure electrical activity generated by translating chemical variation in brain into voltage. EEG signals are measured with multielectrode placed at properly localized part of the brain with either intracranial or extracranial (Scalp EEG) method. EEG analysis has become very important to detect various human diseases. Usually, EEG signals are recorded with the multichannel acquisition module. It is very data intense, time and resource consuming because it should handle a heavy workload of computations. As EEG signal composed of various random signals, independent component analysis (ICA) is considered to be very important method. This paper specifically studies significance of ICA in Epileptic seizure detection using EEG signals. Mostly ICA is used for EEG artifact removal from raw EEG signals. Thus ICA is useful in removing artifacts and improving epileptic seizure detection accuracy.