Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface

In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operation...

Full description

Bibliographic Details
Main Authors: Arvaneh, Mahnaz, Guan, Cuntai, Ang, Kai Keng, Quek, Chai
Other Authors: School of Computer Engineering
Format: Conference Paper
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/98790
http://hdl.handle.net/10220/13388
_version_ 1826116513326694400
author Arvaneh, Mahnaz
Guan, Cuntai
Ang, Kai Keng
Quek, Chai
author2 School of Computer Engineering
author_facet School of Computer Engineering
Arvaneh, Mahnaz
Guan, Cuntai
Ang, Kai Keng
Quek, Chai
author_sort Arvaneh, Mahnaz
collection NTU
description In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operational frequency band. To address these issues, this paper proposes a novel Sparse Multi-Frequency Band CSP (SMFBCSP) algorithm optimized using a mutual information-based approach. Compared to the use of the cross-validation-based method which finds the regularization parameters by trial and error, the proposed mutual information-based approach directly computes the optimal regularization parameters such that the computational time is substantially reduced. The experimental results on 11 stroke patients showed that the proposed SMFBCSP significantly outperformed three existing algorithms based on CSP, sparse CSP and filter bank CSP in terms of classification accuracy.
first_indexed 2024-10-01T04:12:38Z
format Conference Paper
id ntu-10356/98790
institution Nanyang Technological University
language English
last_indexed 2024-10-01T04:12:38Z
publishDate 2013
record_format dspace
spelling ntu-10356/987902020-05-28T07:18:15Z Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai School of Computer Engineering IEEE International Conference on Acoustics, Speech and Signal Processing (2012 : Kyoto, Japan) DRNTU::Engineering::Computer science and engineering In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operational frequency band. To address these issues, this paper proposes a novel Sparse Multi-Frequency Band CSP (SMFBCSP) algorithm optimized using a mutual information-based approach. Compared to the use of the cross-validation-based method which finds the regularization parameters by trial and error, the proposed mutual information-based approach directly computes the optimal regularization parameters such that the computational time is substantially reduced. The experimental results on 11 stroke patients showed that the proposed SMFBCSP significantly outperformed three existing algorithms based on CSP, sparse CSP and filter bank CSP in terms of classification accuracy. 2013-09-09T06:32:03Z 2019-12-06T19:59:41Z 2013-09-09T06:32:03Z 2019-12-06T19:59:41Z 2012 2012 Conference Paper https://hdl.handle.net/10356/98790 http://hdl.handle.net/10220/13388 10.1109/ICASSP.2012.6288434 en © 2012 IEEE.
spellingShingle DRNTU::Engineering::Computer science and engineering
Arvaneh, Mahnaz
Guan, Cuntai
Ang, Kai Keng
Quek, Chai
Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title_full Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title_fullStr Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title_full_unstemmed Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title_short Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface
title_sort multi frequency band common spatial pattern with sparse optimization in brain computer interface
topic DRNTU::Engineering::Computer science and engineering
url https://hdl.handle.net/10356/98790
http://hdl.handle.net/10220/13388
work_keys_str_mv AT arvanehmahnaz multifrequencybandcommonspatialpatternwithsparseoptimizationinbraincomputerinterface
AT guancuntai multifrequencybandcommonspatialpatternwithsparseoptimizationinbraincomputerinterface
AT angkaikeng multifrequencybandcommonspatialpatternwithsparseoptimizationinbraincomputerinterface
AT quekchai multifrequencybandcommonspatialpatternwithsparseoptimizationinbraincomputerinterface