NDLI: Neural network approach towards pattern classification using fMRI activation maps

Content Provider	IEEE Xplore Digital Library
Author	Suma, H.N. Murali, S.
Copyright Year	2008
Description	Author affiliation: B M S Coll. of Eng., Bangalore (Suma, H.N.)
Abstract	The activity patterns in fMRI data represent execution of different physical and mental tasks. Each of these patterns is unique and located in specific location in the brain. The main aim of analyzing these datasets is to localize the areas of the brain that have been activated in a given experiment. The basic analysis involves carrying out a statistical test for activation at thousands of locations in the brain. In this paper an attempt is made to develop and train classifiers based on the subjectspsila fMRI sequences in order to predict the tasks performed. The fMRI data set is huge and also the data size for different tasks is dimensionally dissimilar. Dimensionality reduction of high dimensional data is useful for three general reasons; it reduces computational requirements for subsequent operations on the data, eliminates redundancies in the data, and, in cases where the feature data set dimensionality doesnpsilat match then a common dimension is to be arrived at with the available data. All three reasons apply here, and motivate the use of Principal Component Analysis (PCA), a standard method for creating uncorrelated variables from best-fitting linear combinations of the variables in the raw data. The depth information data is extracted using Statistical Parametric mapping (SPM). The templates comprising of principal components represent individual activity. These are then fed to the back propagation training algorithm. The trained network is capable of classifying the test pattern into the corresponding defined class.
Starting Page	288
Ending Page	293
File Size	841071
Page Count	6
File Format	PDF
ISBN	9781424416912
DOI	10.1109/ICCCE.2008.4580614
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-13
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain Data analysis Neural networks Pattern classification Data engineering Educational institutions Decoding Timing Testing Principal component 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 Libarray 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

A Novel Approach for VQ Using a Neural Network, Mean Shift, and Principal Component Analysis

Fault Diagnosis of Hydro-Generator Unit via GA-Nonlinear Principal Component Analysis Neural Network

Classifying fingerprint images using neural network: deriving the classification state

Unsupervised nonparametric density estimation: A neural network approach

EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

Clustering Analysis of Gene Data Based on PCA and SOM Neural Networks

A Kernel PCA Radial Basis Function Neural Networks and Application

Feature Selection using a Mixed-Norm Penalty Function

Interactive pattern classification by means of artificial neural networks

Neural network approach towards pattern classification using fMRI activation maps

Similar Documents

A Novel Approach for VQ Using a Neural Network, Mean Shift, and Principal Component Analysis

Fault Diagnosis of Hydro-Generator Unit via GA-Nonlinear Principal Component Analysis Neural Network

Classifying fingerprint images using neural network: deriving the classification state

Unsupervised nonparametric density estimation: A neural network approach

EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

Clustering Analysis of Gene Data Based on PCA and SOM Neural Networks

A Kernel PCA Radial Basis Function Neural Networks and Application

Feature Selection using a Mixed-Norm Penalty Function

Interactive pattern classification by means of artificial neural networks

Neural network approach towards pattern classification using fMRI activation maps