NDLI: EEG signal classification using time-varying autoregressive models and common spatial patterns

Content Provider	IEEE Xplore Digital Library
Author	Gutierrez, D. Salazar-Varas, R.
Copyright Year	2011
Description	Author affiliation: Center of Research and Advanced Studies (Cinvestav) at Monterrey, 66600, Apodaca, Mexico (Gutierrez, D.; Salazar-Varas, R.)
Abstract	The performance of EEG signal classification methods based on Common Spatial Patterns (CSP) depends on the operational frequency bands of the events to be discriminated. This problem has been recently addressed by using a sub-band decomposition of the EEG signals through filter banks. Even though this approach has proven effective, the performance still depends on the number of filters that are stacked and the criteria used to determine their cutoff frequencies. Therefore, we propose an alternative approach based on an eigenstructure decomposition of the signals' time-varying autoregressive (TVAR) models. The eigen-based decomposition of the TVAR representation allows for subject-specific estimation of the principal time-varying frequencies, then such principal eigencomponents can be used in the traditional CSP-based classification. A series of simulations show that the proposed classification scheme can achieve high classification rates under realistic conditions, such as low signal-to-noise ratio (SNR), a reduced number of training experiments, and a reduced number of sensors used in the measurements.
Starting Page	6585
Ending Page	6588
File Size	551534
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Brain models Time frequency analysis Noise Sensors Brain computer interfaces
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

Regularized common spatial patterns with generic learning for EEG signal classification

EEG source localization constrained by time varying fMRI

A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies

Comparison of EEG Pattern Classification Methods for Brain-Computer Interfaces

EEG-EMG coherence estimated using time-varying autoregressive models in movement-activated myoclonus in patients with progressive myoclonic epilepsies.

Smooth bilinear classification of EEG

A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest

Common Spatial Pattern Patches: Online evaluation on BCI-naive users

EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface

EEG signal classification using time-varying autoregressive models and common spatial patterns

Similar Documents

Regularized common spatial patterns with generic learning for EEG signal classification

EEG source localization constrained by time varying fMRI

A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies

Comparison of EEG Pattern Classification Methods for Brain-Computer Interfaces

EEG-EMG coherence estimated using time-varying autoregressive models in movement-activated myoclonus in patients with progressive myoclonic epilepsies.

Smooth bilinear classification of EEG

A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest

Common Spatial Pattern Patches: Online evaluation on BCI-naive users

EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface

EEG signal classification using time-varying autoregressive models and common spatial patterns