Abstract:
Electroencephalogram (EEG) signal is an
important source of information for knowing
brain processes. To interpret the brain activity,
Matching Pursuit Based EEG signal
classification is proposed. This system includes
three main components which are Preprocessing,
Feature extraction and Classification. In the
preprocessing step, Wavelet Packet Independent
Component Analysis (WPICA) method is used to
remove some unwanted noise of EEG recording.
Matching Pursuit (MP) with Wavelet Packet
Dictionary is used to extract the features of EEG
signal. The k Nearest Neighbor (kNN) classified
the extracted MP features. In this work, the
Keirn and Aunon EEG dataset is used in the
experiments. The feature extracted from MP
based wavelet packet dictionary achieved over
90% accuracy in two seconds length of
brainwave signal in five mental tasks
classification.
