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BRAIN MACHINE INTERFACE: ANALYSIS OF SEGMENTED EEG SIGNAL CLASSIFICATION USING SHORT-TIME PCA AND RECURRENT NEURAL NETWORKS

Authors: Hema C.R. --- Paulraj M.P --- Nagarajan R --- Sazali Yaacob --- et al.
Journal: Iraqi Journal for Electrical And Electronic Engineering المجلة العراقية للهندسة الكهربائية والالكترونية ISSN: 18145892 Year: 2008 Volume: 4 Issue: 1 Pages: 77-85
Publisher: Basrah University جامعة البصرة

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Abstract

Brain machine interface provides a communication channel between the human brain and anexternal device. Brain interfaces are studied to provide rehabilitation to patients withneurodegenerative diseases; such patients loose all communication pathways except for theirsensory and cognitive functions. One of the possible rehabilitation methods for these patients isto provide a brain machine interface (BMI) for communication; the BMI uses the electricalactivity of the brain detected by scalp EEG electrodes. Classification of EEG signals extractedduring mental tasks is a technique for designing a BMI. In this paper a BMI design using fivemental tasks from two subjects were studied, a combination of two tasks is studied per subject.An Elman recurrent neural network is proposed for classification of EEG signals. Two featureextraction algorithms using overlapped and non overlapped signal segments are analyzed.Principal component analysis is used for extracting features from the EEG signal segments.Classification performance of overlapping EEG signal segments is observed to be better interms of average classification with a range of 78.5% to 100%, while the non overlapping EEGsignal segments show better classification in terms of maximum classifications

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