IJSDR
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: Classification of Four Class Motor Imagery and Hand Movements for Brain Computer Interface
Authors Name: N.Gowri Priya , S.Anu Priya , V.Dhivya , M.D.Ranjitha , P. Sudev
Unique Id: IJSDR1704011
Published In: Volume 2 Issue 4, April-2017
Abstract: In this paper,four class motor imagery and hand movements classification has been done for brain computer interface.In this project, we proposed an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a ENOBIO DEVICE.EEG signals were acquired on the Enobio device, with all 8 channels (F3, F4, FZ, P3, P4, CZ, C3 and C4) in alpha and beta rhythm in order to establish the active networks. Six volunteers were participated, the volunteers were instructed to perform motor imagery tasks and movements using both hands, and both legs. It is that EEG represents the brain activity by the electrical voltage fluctuations along the scalp using 8 Electrodes system. It uses advanced feature extraction techniques and machine learning algorithms. In this work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed hand and leg movements through SVM classification algorithm. The EEG dataset used in this research was created and data was preprocessed using the MATLAB toolbox. An important part of a brain-computer interface is an algorithm for classifying different commands that the user may want to execute. The goal of this is to implement an algorithm that would be able to classify two different hand and leg movement tasks. The features such as Mean, Variance ,Standard Deviation,Discrete wavelet transform have been extracted and finally classification is done using SVM classifier and the greatest accuracy is obtained.
Keywords: BCI, SVM, EEG, ENOBIO.
Cite Article: "Classification of Four Class Motor Imagery and Hand Movements for Brain Computer Interface", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.2, Issue 4, page no.68 - 72, April-2017, Available :http://www.ijsdr.org/papers/IJSDR1704011.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR1704011
Registration ID:170146
Published In: Volume 2 Issue 4, April-2017
DOI (Digital Object Identifier):
Page No: 68 - 72
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media
IJSDR

Indexing Partner