NDLI: Neural network-based three-class motor imagery classification using time-domain features for BCI applications

Content Provider	IEEE Xplore Digital Library
Author	Hamedi, M. Salleh, S.-H. Noor, A.M. Mohammad-Rezazadeh, I.
Copyright Year	2014
Description	Author affiliation: Los Angeles & Center for Mind & Brain, Univ. of California, Davis, Davis, CA, USA (Mohammad-Rezazadeh, I.) \|\| Center for Biomed. Eng., Univ. Teknol. Malaysia Johor Bahru, Johor Bahru, Malaysia (Hamedi, M.; Salleh, S.-H.; Noor, A.M.)
Abstract	Many studies have reported the usefulness of motor imagery (MI) electroencephalogram (EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly characterized by the average of event-related changes of brain activity at specific frequency bands; but, temporal features of EEG have rarely been considered to identify different mental states of BCIs' users. Additionally, complex classification techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online applications. This paper investigated the application of neural network-based algorithms to classify three-class MIs by utilizing EEG time-domain features. Integrated EEG (IEEG) and Root Mean Square (RMS) features were extracted from EEG signals. Then, Multilayer Perceptron and Radial Basis Function Neural Networks were employed to classify the features. The discrimination ratio of such features were examined and compared through different classifiers. Moreover, the robustness of classifiers was investigated and compared. The results of this study indicated that RMS was more capable than IEEG for characterizing MI movements and RBF was more accurate and faster than MLP. The effectiveness of IEEG and RMS features and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI tasks in both terms of accuracy (88.96%) and training time (0.5 second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less accuracy.
Sponsorship	IEEE Malaysia Sect.
Starting Page	204
Ending Page	207
File Size	243695
Page Count	4
File Format	PDF
e-ISBN	9781479920273
DOI	10.1109/TENCONSpring.2014.6863026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-14
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Motor Imagery Time-Domain Feature Accuracy Brain Computer Interface Electroencephalogram Classification Feature extraction Electroencephalography Classification algorithms Time-domain analysis
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

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Evaluation of time-domain features for motor imagery movements using FCM and SVM

A Study and Performance Analysis of Three Paradigms of Wavelet Coefficients Combinations in Three-Class Motor Imagery Based BCI

EEG feature extraction and pattern classification based on motor imagery in brain-computer interface

Multifeature Analysis in Motor Imagery EEG Classification

Determining AR order for BCI based on motor imagery

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

Improved classification of motor imagery datasets for BCI by using approximate entropy and WOSF features

Identification of three mental states using a motor imagery based brain machine interface

Neural network-based three-class motor imagery classification using time-domain features for BCI applications

Similar Documents

Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Evaluation of time-domain features for motor imagery movements using FCM and SVM

A Study and Performance Analysis of Three Paradigms of Wavelet Coefficients Combinations in Three-Class Motor Imagery Based BCI

EEG feature extraction and pattern classification based on motor imagery in brain-computer interface

Multifeature Analysis in Motor Imagery EEG Classification

Determining AR order for BCI based on motor imagery

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

Improved classification of motor imagery datasets for BCI by using approximate entropy and WOSF features

Identification of three mental states using a motor imagery based brain machine interface

Neural network-based three-class motor imagery classification using time-domain features for BCI applications