Classification of Five Control/Non-control States to Enhance Asynchronous Brain-Computer Interfaces

Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theo...

Full description

Saved in:
Bibliographic Details
Published in:WRC Symposium on Advanced Robotics and Automation (Online) pp. 51 - 54
Main Authors: Liu, Xueshuo, Zhang, Qian, Li, Jiaxin, Wang, Hantao, Zhao, Jing
Format: Conference Proceeding
Language:English
Published: IEEE 23.08.2024
Subjects:
Accuracy
Brain-computer interfaces
Complex networks
Decision making
Electroencephalography
Feature extraction
Optimization methods
ISSN:2835-3358
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theory-based decision fusion (DST-DF) method integrating decisions from two asynchronous algorithms for improved classification accuracy. An individualized space-frequency based complex network (ISF-OCN) algorithm and an Irregular-resampling auto-spectral analysis (IRASA) algorithm were used to extract control/non-control features for classification. The DST-DF method constructed the basic probability assignment (BPA) functions for them and assigned weights considering their varied importance in decision making. The weights were optimized using a single objective Bayesian optimization method, and were assigned to the decisions of ISF-OCN and IRASA for D-S fusion. The experimental results of 10 subjects showed that the proposed method obtained higher accuracy (93.92%) by integrating the ISF-OCN (78.87%) and IRASA (91.20%) algorithms.
AbstractList Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theory-based decision fusion (DST-DF) method integrating decisions from two asynchronous algorithms for improved classification accuracy. An individualized space-frequency based complex network (ISF-OCN) algorithm and an Irregular-resampling auto-spectral analysis (IRASA) algorithm were used to extract control/non-control features for classification. The DST-DF method constructed the basic probability assignment (BPA) functions for them and assigned weights considering their varied importance in decision making. The weights were optimized using a single objective Bayesian optimization method, and were assigned to the decisions of ISF-OCN and IRASA for D-S fusion. The experimental results of 10 subjects showed that the proposed method obtained higher accuracy (93.92%) by integrating the ISF-OCN (78.87%) and IRASA (91.20%) algorithms.
Author Zhang, Qian
Li, Jiaxin
Wang, Hantao
Zhao, Jing
Liu, Xueshuo
Author_xml – sequence: 1
  givenname: Xueshuo
  surname: Liu
  fullname: Liu, Xueshuo
  email: 1120126113@qq.com
  organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004
– sequence: 2
  givenname: Qian
  surname: Zhang
  fullname: Zhang, Qian
  email: zhangqian0321@163.com
  organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004
– sequence: 3
  givenname: Jiaxin
  surname: Li
  fullname: Li, Jiaxin
  email: ljx0508lim@163.com
  organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004
– sequence: 4
  givenname: Hantao
  surname: Wang
  fullname: Wang, Hantao
  email: 1758400205@qq.com
  organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004
– sequence: 5
  givenname: Jing
  surname: Zhao
  fullname: Zhao, Jing
  email: zhaoj0104@ysu.edu.cn
  organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004
BookMark eNo1kM1OAjEYRavRRETewEXjfqCd_i_HCSgJ0QQ0LkmnfA010JJpMeHtJUE3957cxVnce3QTUwSEnigZU0rM5GvZrpplIzmValyTmo8pkVoozq_QyCijGaOCSErINRrUmomKMaHv0Cjnb0IIqylVtRgg1-5szsEHZ0tIESePZ-EHcJti6dNu8pZi5S6MV8UWyLgkPI1bGx3gJp-i2_YppmPGz70NsWrT_nAs0ON5PKe3DvIDuvV2l2H010P0OZt-tK_V4v1l3jaLKlAlS7XRneRKU02E3xDjlAAtrGWKs_NC9KbupHUStAHwlHfWdNJ3nVHcUa6pZ0P0ePEGAFgf-rC3_Wn9_wv7BZ6lW6I
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/WRCSARA64167.2024.10685744
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798331506100
EISSN 2835-3358
EndPage 54
ExternalDocumentID 10685744
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  funderid: 10.13039/501100001809
GroupedDBID 6IE
6IL
6IN
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i176t-d8b64781805fd09c75e85aa374305f08d2b6ac6e89eef14ba9b6fbb974c1481f3
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001327761400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Tue May 06 03:33:11 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i176t-d8b64781805fd09c75e85aa374305f08d2b6ac6e89eef14ba9b6fbb974c1481f3
PageCount 4
ParticipantIDs ieee_primary_10685744
PublicationCentury 2000
PublicationDate 2024-Aug.-23
PublicationDateYYYYMMDD 2024-08-23
PublicationDate_xml – month: 08
  year: 2024
  text: 2024-Aug.-23
  day: 23
PublicationDecade 2020
PublicationTitle WRC Symposium on Advanced Robotics and Automation (Online)
PublicationTitleAbbrev WRC SARA
PublicationYear 2024
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003211725
Score 1.8805064
Snippet Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown...
SourceID ieee
SourceType Publisher
StartPage 51
SubjectTerms Accuracy
Brain-computer interfaces
Complex networks
Decision making
Electroencephalography
Feature extraction
Optimization methods
Title Classification of Five Control/Non-control States to Enhance Asynchronous Brain-Computer Interfaces
URI https://ieeexplore.ieee.org/document/10685744
WOSCitedRecordID wos001327761400009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELVoxcAEiCC-5YHVbRLHHxlL1YqpqgqIbpXtnEWXBLVpJf49tpMUMTCwRR6i6E6O753fu4fQY0JBskJLB0tSSzIDhihBNeHct3G4TJXWwWxCzGZyucznrVg9aGEAIJDPYOAfw11-UZmdb5W5Hc4lE1nWQz0heCPWOjRUqIMyImXtYNEkzofvi_HLaDHiruYQDgmm2aB7wS8rlXCSTE__-Q1nKPrR5OH54bQ5R0dQXiATPC092ycEGFcWT93vC48bAvpwVpWkJaPjpqzEdYUn5YfPNR5tv0rjh-M69I-fvFcE6UwecOgUWs_XitDbdPI6fiatbQJZJ4LXpJA6CEhlzGwR50Ywlw6lqPDTvWwsi1RzZTjIHMAmmVa55lZrByyMw0aJpZeoX1YlXCEsmabWAHVlSpwpt3dBWc4KEVsAzkVyjSIfodVnMxlj1QXn5o_1W3Ti8-B7sim9Q_16s4N7dGz29Xq7eQj5_AZheqKE
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELWgIMEEiCK-8cDqNok_M5aqVRGlqkoR3SrbsUWXBLUpEv8e20mLGBjYIg9RdCfH987v3QPgPsZG0EwJB0sSi4g2GkmOFWLMt3GYSKRSwWyCj0ZiNkvHtVg9aGGMMYF8Zlr-MdzlZ4Ve-1aZ2-FMUE7ILtijhCRRJdfatlSwAzM8ofVo0ThK22-T7ktn0mGu6uAOCyaktXnFLzOVcJb0j_75Fceg-aPKg-PteXMCdkx-CnRwtfR8nxBiWFjYdz8w2K0o6O1RkaOajg6rwhKWBezl7z7bsLP6yrUfj-vwP3zwbhFoY_MAQ6_QesZWE7z2e9PuANXGCWgRc1aiTKggIRURtVmUak5dQqTE3M_3spHIEsWkZkakxtiYKJkqZpVy0EI7dBRbfAYaeZGbcwAFVdhqg12hEhHpdq-RltGMR9YYxnh8AZo-QvOPajbGfBOcyz_W78DBYPo8nA8fR09X4NDnxHdoE3wNGuVybW7Avv4sF6vlbcjtNwMVpcs
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=WRC+Symposium+on+Advanced+Robotics+and+Automation+%28Online%29&rft.atitle=Classification+of+Five+Control%2FNon-control+States+to+Enhance+Asynchronous+Brain-Computer+Interfaces&rft.au=Liu%2C+Xueshuo&rft.au=Zhang%2C+Qian&rft.au=Li%2C+Jiaxin&rft.au=Wang%2C+Hantao&rft.date=2024-08-23&rft.pub=IEEE&rft.eissn=2835-3358&rft.spage=51&rft.epage=54&rft_id=info:doi/10.1109%2FWRCSARA64167.2024.10685744&rft.externalDocID=10685744