NDLI: An evaluation of EEG ocular artifact removal with a multi-channel wiener filter based on probabilistic generative model

Content Provider	IEEE Xplore Digital Library
Author	Maki, H. Toda, T. Sakti, S. Neubig, G. Nakamura, S.
Copyright Year	2015
Description	Author affiliation: Grad. Sch. of Inf. Sci., NAIST, Ikoma, Japan (Maki, H.) \|\| Fac. of Grad. Sch. of Inf. Sci., NAIST, Ikoma, Japan (Toda, T.; Sakti, S.; Neubig, G.; Nakamura, S.)
Abstract	Data contamination by ocular artifacts such as eye blinks and eye movements is a major barrier that must be overcome when attempting to analyze electroencephalogram (EEG) and event-related potential (ERP) data. To handle this problem, a number of artifact removal methods has been proposed. Specifically, we focus on a method using a multi-channel Wiener filters based on a probabilistic generative model. This method assumes that the observed signal is the sum of multiple signals elicited by psychological or physical events, and separates the observed signal into each event signal using estimated model parameters. Based on this scheme, we have proposed a model parameter estimation method using prior information of each event signal. In this paper, we examine the potential of this model to deal with highly contaminated signals by collecting EEG data intentionally contaminated by eye blinks and relatively clean ERP data, and using them as prior information of each event signal. We conducted an experimental evaluation using a classical attention task. The results showed the proposed method effectively enhances the target ERP component while reducing the contamination caused by eye blinks.
Starting Page	2775
Ending Page	2778
File Size	1006939
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318967
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Brain modeling Time-frequency analysis Probabilistic logic Covariance matrices Mathematical model Electrodes
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

EEG signal enhancement using multi-channel wiener filter with a spatial correlation prior

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

An application of Gaussian processes on ocular artifact removal from EEG

Comparison of three ICA algorithms for ocular artifact removal from TMS-EEG recordings

Classification of hand movement direction based on EEG high-gamma activity

A global approach for automatic artifact removal for standard EEG record

Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking

A reduced rank approach for covariance matrix estimation in EEG signal classification

Online EEG channel weighting for detection of seizures in the neonate

An evaluation of EEG ocular artifact removal with a multi-channel wiener filter based on probabilistic generative model

Similar Documents

EEG signal enhancement using multi-channel wiener filter with a spatial correlation prior

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

An application of Gaussian processes on ocular artifact removal from EEG

Comparison of three ICA algorithms for ocular artifact removal from TMS-EEG recordings

Classification of hand movement direction based on EEG high-gamma activity

A global approach for automatic artifact removal for standard EEG record

Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking

A reduced rank approach for covariance matrix estimation in EEG signal classification

Online EEG channel weighting for detection of seizures in the neonate

An evaluation of EEG ocular artifact removal with a multi-channel wiener filter based on probabilistic generative model