NDLI: Multi-Class Support Vector Machines for Brain Neural Signals Recognition

Content Provider	IEEE Xplore Digital Library
Author	Huijuan Fang Yongji Wang Jian Huang Jiping He
Copyright Year	2006
Description	Author affiliation: Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan (Huijuan Fang; Yongji Wang; Jian Huang; Jiping He)
Abstract	It is promising to control neuroprosthetic devices by the activity of cortical neurons when appropriate algorithms are use to decode intended movement. In this paper, a multi-class support vector machines (SVMs) algorithm of a binary tree recognition strategy is used to analyze the motor cortical neuronal signals. The neural ensemble data were recorded simultaneously with kinematics of arm movement while the monkey performed reaching tasks from the center position to eight peripheral targets in a three-dimensional (3D) virtual environment. The SVMs based method was applied to classify the neural ensemble firing rate patterns into eight classes. The performance of the SVMs based neural activity recognition was compared with that of the learning vector quantization (LVQ) approach. The results show that the SVMs can achieve higher accuracy with less computational time, which demonstrates that the SVMs algorithm is a suitable approach for brain neural signals recognition
Starting Page	9940
Ending Page	9944
File Size	139993
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Neural prosthesis Neurons Decoding Binary trees Signal analysis Algorithm design and analysis Kinematics Virtual environment Vector quantization Support vector machines (SVMs) Extraction algorithm Neural ensembles
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

Multi-class Classifier Based on Support Vector Machines and Similar Binary Tree

Binary Classification by SVM based neural Trees and Nonlinear SVMs

Automatic construction algorithm for multi-class support vector machines with binary tree architecture (2006).

An Enhanced Centered Binary Tree of SVMs Algorithm for Phoneme Recognition

Support Vector Machines for Multi-class Pattern Recognition 1. K-class Pattern Recognition 2. Solving K-class Problems with Binary Svms

Modulation recognition algorithm of digital signal based on support vector machine

Radar emitter signal recognition based on support vector machines

EMG pattern recognition using Support Vector Machines classifier for myoelectric control purposes

Precision of multi-class classification methods for support vector machines

Multi-Class Support Vector Machines for Brain Neural Signals Recognition

Similar Documents

Multi-class Classifier Based on Support Vector Machines and Similar Binary Tree

Binary Classification by SVM based neural Trees and Nonlinear SVMs

Automatic construction algorithm for multi-class support vector machines with binary tree architecture (2006).

An Enhanced Centered Binary Tree of SVMs Algorithm for Phoneme Recognition

Support Vector Machines for Multi-class Pattern Recognition 1. K-class Pattern Recognition 2. Solving K-class Problems with Binary Svms

Modulation recognition algorithm of digital signal based on support vector machine

Radar emitter signal recognition based on support vector machines

EMG pattern recognition using Support Vector Machines classifier for myoelectric control purposes

Precision of multi-class classification methods for support vector machines

Multi-Class Support Vector Machines for Brain Neural Signals Recognition