NDLI: Classification of motor imagery tasks for electrocorticogram based brain-computer interface

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Classification of motor imagery tasks for electrocorticogram based brain-computer interface

Content Provider	Springer Nature Link
Author	Xu, Fangzhou Zhou, Weidong Zhen, Yilin Yuan, Qi
Copyright Year	2014
Abstract	In the present study, we propose a novel scheme for motor imagery (MI) classification of multichannel electrocorticogram (ECoG) recordings from patients with medically intractable focal epilepsy.This scheme proposes a combination of the two features which includes autoregressive (AR) model coefficients and local binary pattern (LBP) operators. It can provide spatial resolution and angular space information. Then the gradient boosting (GB) in conjunction with ordinary least squares (OLS) algorithm is employed as the classifier to improve the performance of MI classification for ECoG based Brain Computer Interface (BCI) system.Experimental results on the BCI Competition III data set I indicate that the novel method has excellent performance and yields a cross-validation accuracy of 88.8% and accuracy of 93%, respectively.From the experimental results and comparative studies, we can infer that the scheme may serve as a good MI classification tool for a better tradeoff between the classification accuracy and computational complexity.
Starting Page	149
Ending Page	157
Page Count	9
File Format	PDF
ISSN	20939868
Journal	Biomedical Engineering Letters
Volume Number	4
Issue Number	2
e-ISSN	2093985X
Language	English
Publisher	The Korean Society of Medical and Biological Engineering
Publisher Date	2014-07-12
Publisher Place	Korea
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Local binary pattern (LBP) Brain-computer interface (BCI) Electrocorticogram (ECoG) Motor imagery (MI) Biomedical Engineering Biophysics and Biological Physics Biomedicine general Medical and Radiation Physics
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Representation Learning for Motor Imagery Recognition with Deep Neural Network

Comparative Study of Data Classification Methods Between EEG and ECoG Used to BCI

Recent functional near infrared spectroscopy based brain computer interface systems: Developments, applications and challenges

Classification of motor imagery ecog signals using support vector machine for brain computer interface

Progress in speech decoding from the electrocorticogram

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Comparison of pre-amplifier topologies for use in brain-computer interface with capacitively-coupled EEG electrodes

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

Classification of motor imagery tasks for electrocorticogram based brain-computer interface

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Classification of motor imagery ecog signals using support vector machine for brain computer interface

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