Pattern Classification of EEG Signals on Different States of Cognition Using Linear and Nonlinear Classifiers

摘要

There is a need to analyze and interpret the EEG data obtained from the brain which has its importance in various fields and applications. In this study we have acquired the EEG signal from the subjects while performing different tasks and then use pattern classification to differentiate the various tasks. Artifacts in the EEG signal are removed in the preprocessing stage. Features extracted from EEG datasets of various subjects were used as input to the neural network for training, validation and classification. Nearest neighbor and feed forward Neural Networks were used for classification and their results were compared.