NDLI: Feature space embeddings for extracting structure from single channel wake EEG using RBF networks

Content Provider	IEEE Xplore Digital Library
Author	Lowe, D.
Copyright Year	1998
Description	Author affiliation: Neural Comput. Res. Group, Aston Univ., Birmingham, UK (Lowe, D.)
Abstract	This paper concerns structure extraction for the analysis of real-world single channel wake EEG signals. The long term objective is a study of vigilance in wake EEG. The authors compare the use of PCA and ICA-motivated embeddings based on single channel EEG data, to increase signal-to-noise ratio and to obtain a robust feature space which is more amenable to characterisation by neural networks. The work is novel in several respects: the use of unsupervised data (most work in this area is through evoked potential response experiments), the treatment of EEG by short window embeddings directly from the time domain (most work averages 30 second segments of power spectra), the comparison of ICA and PCA embeddings on single channel data (most ICA work so far reported has employed multiple sensor channels) and the use of a 'NeuroScale' architecture for topographic feature extraction.
Starting Page	428
Ending Page	437
File Size	607859
Page Count	10
File Format	PDF
ISBN	078035060X
ISSN	10893555
DOI	10.1109/NNSP.1998.710673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-09-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Radial basis function networks Independent component analysis Brain Frequency Signal analysis Feature extraction Signal processing Data mining 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 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

Reduced Complexity Space-Time-Frequency Model for Multi-Channel EEG and Its Applications

Face Recognition Based on FastICA and RBF Neural Networks

Bayesian radial basis function network for modeling fMRI data

Independent component extraction methods in biosignal processing

ICA-Based Potential Significant Feature Extraction for Market Forecast

ICA-based Potential Significant Feature Extraction for Market Forecast

An empirical centre assignment in RBF network for quantification of anaesthesia using wavelet-domain features

Multichannel EEG-based biometric using improved RBF neural networks

Kernel ICA Feature Extraction for Spectral Recognition of Celestial Objects

Feature space embeddings for extracting structure from single channel wake EEG using RBF networks

Similar Documents

Reduced Complexity Space-Time-Frequency Model for Multi-Channel EEG and Its Applications

Face Recognition Based on FastICA and RBF Neural Networks

Bayesian radial basis function network for modeling fMRI data

Independent component extraction methods in biosignal processing

ICA-Based Potential Significant Feature Extraction for Market Forecast

ICA-based Potential Significant Feature Extraction for Market Forecast

An empirical centre assignment in RBF network for quantification of anaesthesia using wavelet-domain features

Multichannel EEG-based biometric using improved RBF neural networks

Kernel ICA Feature Extraction for Spectral Recognition of Celestial Objects

Feature space embeddings for extracting structure from single channel wake EEG using RBF networks