NDLI: EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery

Content Provider	IEEE Xplore Digital Library
Author	Akrami, A. Solhjoo, S. Motie-Nasrabadi, A. Hashemi-Golpayegani, M.-R.
Copyright Year	2005
Description	Author affiliation: Biomed. Eng. Fac., Amirkabir Univ. of Technol., Tehran (Akrami, A.; Solhjoo, S.)
Abstract	Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "brain-computer interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, neural network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets
Starting Page	4626
Ending Page	4629
File Size	223918
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Biomedical engineering Feature extraction Brain Biomedical signal processing Frequency domain analysis Fourier transforms Signal resolution Communication systems Biological neural networks
Content Type	Text
Resource Type	Article

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