NDLI: Developing a logistic regression model with cross-correlation for motor imagery signal recognition

Content Provider	IEEE Xplore Digital Library
Author	Siuly Yan Li Jinglong Wu Jingjing Yang
Copyright Year	2011
Description	Author affiliation: Biomedical Engineering Laboratory, Graduate School of Natural Science and Technology, Okayama University, Japan (Jinglong Wu; Jingjing Yang) \|\| Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, 4350, Australia (Siuly; Yan Li)
Abstract	Classification of motor imagery (MI)-based electroencephalogram (EEG) signals is a key issue for the development of brain-computer interface (BCI) systems. The objective of this study is to develop an algorithm that can distinguish two categories of MI EEG signals. In this paper, we propose a new classification algorithm for two-class MI signals recognition in BCIs. The proposed scheme develops a novel cross-correlation-based feature extractor, which is aided with a logistic regression model. The present method is tested on dataset IVa of BCI Competition III, which contain two-class MI data for five subjects. The performance is objectively computed using a k-fold cross validation (k=10) method on the testing set for each subject. The results of this study are compared with the recently reported eight methods in the literature. The results demonstrate that our proposed method outperforms the eight methods in terms of the average classification accuracy.
Starting Page	502
Ending Page	507
File Size	1131235
Page Count	6
File Format	PDF
ISBN	9781424493234
e-ISBN	9781424493241
DOI	10.1109/ICCME.2011.5876793
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Weight measurement Motor imagery (MI) Cross-correlation technique Brain-computer interface (BCI) Biomedical measurements Classification algorithms Logistic regression model Electroencephalogram (EEG) Object recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

Motor imagery EEG signal classification scheme based on autoregressive reflection coefficients

EEG channel selection based on sequential backward floating search for motor imagery classification

Multifeature Analysis in Motor Imagery EEG Classification

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

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

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

Developing a logistic regression model with cross-correlation for motor imagery signal recognition

Similar Documents

Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

Motor imagery EEG signal classification scheme based on autoregressive reflection coefficients

EEG channel selection based on sequential backward floating search for motor imagery classification

Multifeature Analysis in Motor Imagery EEG Classification

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

Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

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

Developing a logistic regression model with cross-correlation for motor imagery signal recognition