Deep Learning Model for Analyzing EEG Signal Analysis

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE access Ročník 13; s. 91034 - 91045
Hlavní autori: Gupta, Varun, Kumar, Vivek, Prince, Singh, Saurabh, Lee, Young-Seok, Ra, In-Ho
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
band power
Bat-based UNET signal analysis (BUSA)
Brain
Brain modeling
Classification
Classification algorithms
Convolutional neural networks
correlation dimension
Datasets
Deep learning
EEG datasets
Electrodes
Electroencephalography
Emotion recognition
Encoders-Decoders
Feature extraction
Feature selection
Machine learning
Noise reduction
Performance evaluation
Signal analysis
U-shaped encoder-decoder network (UNET)
ISSN:2169-3536, 2169-3536
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique.
AbstractList To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique.
Author Singh, Saurabh
Prince
Gupta, Varun
Kumar, Vivek
Ra, In-Ho
Lee, Young-Seok
Author_xml – sequence: 1
  givenname: Varun
  orcidid: 0000-0002-0390-9266
  surname: Gupta
  fullname: Gupta, Varun
  organization: Department of Electronics and Communication, National Institute of Technology Sikkim, Namchi, India
– sequence: 2
  givenname: Vivek
  orcidid: 0000-0001-9285-2857
  surname: Kumar
  fullname: Kumar, Vivek
  organization: Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Namchi, India
– sequence: 3
  orcidid: 0000-0002-5878-7227
  surname: Prince
  fullname: Prince
  organization: Department of Industrial and Systems Engineering, Dongguk University, Seoul, Republic of Korea
– sequence: 4
  givenname: Saurabh
  surname: Singh
  fullname: Singh, Saurabh
  organization: Department of AI and Bigdata, Woosong University, Daejeon, Republic of Korea
– sequence: 5
  givenname: Young-Seok
  surname: Lee
  fullname: Lee, Young-Seok
  organization: School of Computer and Software, Kunsan National University, Gunsan, Republic of Korea
– sequence: 6
  givenname: In-Ho
  orcidid: 0000-0002-3936-1116
  surname: Ra
  fullname: Ra, In-Ho
  email: ihra@kunsan.ac.kr
  organization: School of Software, Kunsan National University, Gunsan, Republic of Korea
BookMark eNpNUE1PAjEQbQwmovIL9LCJZ3Db6cf2SBCVBOMBPTelnZIluMUWDvjrXVximMvMvMx7M_OuSa-JDRJyR8sRpaV-HE8m08VixEomRiAkKFlekD6jUg9BgOyd1VdkkPO6bKNqIaH6RDwhbos52tTUzap4ix43RYipGDd2c_g5YtPpS7GoV23fgbnOt-Qy2E3GwSnfkM_n6cfkdTh_f5lNxvOhA0l3Q10F4ZUH1EFRx7znpdQBKHLNqPIWl94uaXuLU0uhUSrPHVMAjKEMwTm4IbNO10e7NttUf9l0MNHW5g-IaWVs2tVug0ZVVqD1oErOubPCouUVOAbOMhqcbLUeOq1tit97zDuzjvvUPpQNMAqaaeCinYJuyqWYc8Lwv5WW5mi36ew2R7vNye6Wdd-xakQ8Y2jFtajgF0Ove7E
CODEN IAECCG
Cites_doi 10.1007/s12652-023-04746-y
10.1007/s11277-022-09678-y
10.1016/j.bspc.2021.102749
10.1016/j.jad.2019.05.070
10.1007/s11277-024-10916-8
10.1504/ijbra.2024.137372
10.1504/IJMEI.2023.129353
10.1016/j.compbiomed.2024.108329
10.1007/s11277-023-10326-2
10.1007/s40031-022-00831-6
10.1007/s40031-022-00783-x
10.1007/s11063-016-9530-1
10.1007/s13755-021-00139-7
10.1007/s11277-024-11409-4
10.1016/j.cub.2018.11.052
10.1007/s10470-018-1249-7
10.1504/IJBRA.2025.145103
10.1109/LSP.2019.2906824
10.1007/s11042-018-5885-9
10.1007/s11277-022-10076-7
10.1109/ICCP.2017.8117036
10.1016/j.future.2019.06.027
10.1007/s12652-023-04715-5
10.1016/j.compbiomed.2024.108510
10.1007/s11042-023-15462-2
10.1016/j.compbiomed.2021.104757
10.1080/03772063.2019.1575292
10.1007/s12652-023-04582-0
10.1007/s11277-020-07253-x
10.1007/s11277-020-07675-7
10.1016/j.eswa.2020.113285
10.1049/iet-bmt.2017.0160
10.1080/03772063.2023.2220698
10.1504/IJBRA.2024.137369
10.1504/IJESMS.2025.144881
10.1504/IJBRA.2022.121758
10.1007/s11277-021-09389-w
10.1016/j.eswa.2023.122777
10.1007/s12652-022-04469-6
10.1007/s40031-021-00693-4
10.1016/j.clinph.2020.04.172
10.1016/j.eswa.2018.08.031
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID 97E
ESBDL
RIA
RIE
AAYXX
CITATION
7SC
7SP
7SR
8BQ
8FD
JG9
JQ2
L7M
L~C
L~D
DOA
DOI 10.1109/ACCESS.2025.3563760
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE Xplore Open Access Journals
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore (IEEE/IET Electronic Library - IEL)
CrossRef
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Engineered Materials Abstracts
METADEX
Technology Research Database
Materials Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
Materials Research Database
Engineered Materials Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Advanced Technologies Database with Aerospace
METADEX
Computer and Information Systems Abstracts Professional
DatabaseTitleList Materials Research Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: RIE
  name: IEEE Xplore
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2169-3536
EndPage 91045
ExternalDocumentID oai_doaj_org_article_78a5ead370444ca5aea483c23ca21fc6
10_1109_ACCESS_2025_3563760
10974958
Genre orig-research
GrantInformation_xml – fundername: “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (MOE)
  grantid: 2023RIS-008
GroupedDBID 0R~
4.4
5VS
6IK
97E
AAJGR
ABAZT
ABVLG
ACGFS
ADBBV
AGSQL
ALMA_UNASSIGNED_HOLDINGS
BCNDV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
EBS
EJD
ESBDL
GROUPED_DOAJ
IPLJI
JAVBF
KQ8
M43
M~E
O9-
OCL
OK1
RIA
RIE
RNS
AAYXX
CITATION
7SC
7SP
7SR
8BQ
8FD
JG9
JQ2
L7M
L~C
L~D
ID FETCH-LOGICAL-c361t-98f5d7d3e9f71c2dd4069f31e49217daebdab1081c7b59e67d4c273322e6ffcc3
IEDL.DBID RIE
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001499536700014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2169-3536
IngestDate Fri Oct 03 12:51:21 EDT 2025
Wed Oct 08 03:46:55 EDT 2025
Sat Nov 29 07:49:04 EST 2025
Wed Aug 27 01:49:40 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
License https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c361t-98f5d7d3e9f71c2dd4069f31e49217daebdab1081c7b59e67d4c273322e6ffcc3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-3936-1116
0000-0002-5878-7227
0000-0001-9285-2857
0000-0002-0390-9266
OpenAccessLink https://ieeexplore.ieee.org/document/10974958
PQID 3213929345
PQPubID 4845423
PageCount 12
ParticipantIDs doaj_primary_oai_doaj_org_article_78a5ead370444ca5aea483c23ca21fc6
crossref_primary_10_1109_ACCESS_2025_3563760
ieee_primary_10974958
proquest_journals_3213929345
PublicationCentury 2000
PublicationDate 20250000
2025-00-00
20250101
2025-01-01
PublicationDateYYYYMMDD 2025-01-01
PublicationDate_xml – year: 2025
  text: 20250000
PublicationDecade 2020
PublicationPlace Piscataway
PublicationPlace_xml – name: Piscataway
PublicationTitle IEEE access
PublicationTitleAbbrev Access
PublicationYear 2025
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref31
ref30
ref11
ref33
ref10
ref32
(ref28) 2024
ref2
ref1
ref17
ref39
ref16
ref38
ref19
ref18
Biasiucci (ref13) 2024; 251
ref24
ref23
ref45
ref26
ref25
ref20
ref42
ref41
ref22
ref44
ref21
ref43
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
References_xml – ident: ref10
  doi: 10.1007/s12652-023-04746-y
– ident: ref24
  doi: 10.1007/s11277-022-09678-y
– ident: ref7
  doi: 10.1016/j.bspc.2021.102749
– volume: 251
  year: 2024
  ident: ref13
  article-title: EEG based automated detection of seizure using machine learning approach and traditional features
  publication-title: Expert Syst. Appl.
– ident: ref39
  doi: 10.1016/j.jad.2019.05.070
– ident: ref23
  doi: 10.1007/s11277-024-10916-8
– ident: ref29
  doi: 10.1504/ijbra.2024.137372
– ident: ref43
  doi: 10.1504/IJMEI.2023.129353
– ident: ref17
  doi: 10.1016/j.compbiomed.2024.108329
– ident: ref2
  doi: 10.1007/s11277-023-10326-2
– ident: ref41
  doi: 10.1007/s40031-022-00831-6
– ident: ref19
  doi: 10.1007/s40031-022-00783-x
– ident: ref35
  doi: 10.1007/s11063-016-9530-1
– ident: ref38
  doi: 10.1007/s13755-021-00139-7
– ident: ref45
  doi: 10.1007/s11277-024-11409-4
– ident: ref1
  doi: 10.1016/j.cub.2018.11.052
– volume-title: Data Acquisition, Loggers, Amplifiers, Transducers, Electrodes | BIOPAC
  ident: ref30
– ident: ref26
  doi: 10.1007/s10470-018-1249-7
– ident: ref40
  doi: 10.1504/IJBRA.2025.145103
– ident: ref37
  doi: 10.1109/LSP.2019.2906824
– ident: ref33
  doi: 10.1007/s11042-018-5885-9
– ident: ref25
  doi: 10.1007/s11277-022-10076-7
– ident: ref4
  doi: 10.1109/ICCP.2017.8117036
– ident: ref8
  doi: 10.1016/j.future.2019.06.027
– ident: ref9
  doi: 10.1007/s12652-023-04715-5
– ident: ref15
  doi: 10.1016/j.compbiomed.2024.108510
– ident: ref31
  doi: 10.1007/s11042-023-15462-2
– ident: ref6
  doi: 10.1016/j.compbiomed.2021.104757
– volume-title: Physionet Database
  year: 2024
  ident: ref28
– ident: ref32
  doi: 10.1080/03772063.2019.1575292
– ident: ref42
  doi: 10.1007/s12652-023-04582-0
– ident: ref20
  doi: 10.1007/s11277-020-07253-x
– ident: ref22
  doi: 10.1007/s11277-020-07675-7
– ident: ref3
  doi: 10.1016/j.eswa.2020.113285
– ident: ref5
  doi: 10.1049/iet-bmt.2017.0160
– ident: ref27
  doi: 10.1080/03772063.2023.2220698
– ident: ref16
  doi: 10.1504/IJBRA.2024.137369
– ident: ref44
  doi: 10.1504/IJESMS.2025.144881
– ident: ref12
  doi: 10.1504/IJBRA.2022.121758
– ident: ref18
  doi: 10.1007/s11277-021-09389-w
– ident: ref14
  doi: 10.1016/j.eswa.2023.122777
– ident: ref11
  doi: 10.1007/s12652-022-04469-6
– ident: ref21
  doi: 10.1007/s40031-021-00693-4
– ident: ref34
  doi: 10.1016/j.clinph.2020.04.172
– ident: ref36
  doi: 10.1016/j.eswa.2018.08.031
SSID ssj0000816957
Score 2.347819
Snippet To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts,...
SourceID doaj
proquest
crossref
ieee
SourceType Open Website
Aggregation Database
Index Database
Publisher
StartPage 91034
SubjectTerms Accuracy
band power
Bat-based UNET signal analysis (BUSA)
Brain
Brain modeling
Classification
Classification algorithms
Convolutional neural networks
correlation dimension
Datasets
Deep learning
EEG datasets
Electrodes
Electroencephalography
Emotion recognition
Encoders-Decoders
Feature extraction
Feature selection
Machine learning
Noise reduction
Performance evaluation
Signal analysis
U-shaped encoder-decoder network (UNET)
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV27TsMwFLVQxQAD4lFEoKAMjITG8XsspYWpQgKkbpbjR1UJFdQWBr4e23FREAMLqxXFuefG9jmWfS4Al7UVGteVzwAvcdi6wQW3nsgpzJBTnFrDm2ITbDLh06l4aJX6CmfCGnvgBrg-44r4aBELxmZaEWUV5khXSKsKOh3Ntj3raYmpOAdzSAVhyWYIlqI_GA59RF4QVuQaERrOgvxYiqJjfyqx8mtejovNeB_sJZaYD5qvOwBbdnEIdlvegUeA3Fr7lid71Fkeapq95J6B5tFm5DO0jUZ3-eN8Ft-UvEe64Hk8ehreF6kGQqERhetCcEcMM8gKx6CujAk3VR2CFgsvJoyytVE19OFqVhNhKTNYe0bih6mlzmmNjkFn8bqwJyAPTn4YKa85HMK6pkI5ZjhViFqsiCkzcLWBQ741VhcySoRSyAY9GdCTCb0M3ATIvh8NPtWxwWdPpuzJv7KXgW4AvNWf1zeC8Az0NhmQaVCtJKpgYHMIk9P_6PsM7IR4mv2UHuisl-_2HGzrj_V8tbyI_9MXuiDLXg
  priority: 102
  providerName: Directory of Open Access Journals
Title Deep Learning Model for Analyzing EEG Signal Analysis
URI https://ieeexplore.ieee.org/document/10974958
https://www.proquest.com/docview/3213929345
https://doaj.org/article/78a5ead370444ca5aea483c23ca21fc6
Volume 13
WOSCitedRecordID wos001499536700014&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
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2169-3536
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000816957
  issn: 2169-3536
  databaseCode: DOA
  dateStart: 20130101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2169-3536
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000816957
  issn: 2169-3536
  databaseCode: M~E
  dateStart: 20130101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4B4tAeeLRULC_lwLH7SPw-wrLAhRVSW4mb5dhjhFQtCBYOHPjteByDFlU9cIkiy0nsb-x4Zuz5BuCwReN52yQJ6BEn1w3va0yKnOOKRaclBt0lm1DTqb66MpclWD3HwiBiPnyGA7rNe_nh1j-Sq2xIu6VJodfLsKyU6oK13h0qlEHCCFWYhVLV4dF4nDqRbMBGDJiQdPzjw-qTSfpLVpV_fsV5fTld_2TLNmCtKJLVUSf5TVjC2Tf4ukAv-B3ECeJdVRhUrytKe_a3SkpqlZlInqlsMjmrft1c5zcVepIt-HM6-T0-75c0CX3PZD3vGx1FUIGhiar2TQgUzBpZjdwkeyM4bINr6wSPV60wKFXgPiktaSajjNF79gNWZrcz3IaKyP44c8ksiYz7VhoXVdDSMYnciTDqwc83-Oxdx4ZhsxUxMrZD2xLatqDdg2OC-L0qUVnngoSdLTPDKu1EGs5MEXOdd8Kh45r5hnnX1NHLHmwR3gvf66Duwd6bxGyZdw-WNTUpfIyLnf88tgtfqImdF2UPVub3j7gPq_5pfvNwf5BN8nS9eJkc5OH1Clrryp0
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB1RQCo9UGhBbEtLDhxZ2MTfR7ospSqskKASN8uxxwgJLQgWDvx6PI5BINQDt8hyEueNJ54Ze94AbLZoPG-bJAE94BS64X2NyZBzXLHotMSgu2ITajzWZ2fmuCSr51wYRMyHz3CbLvNefrjydxQq26Hd0mTQ6w8wJzhv6i5d6zmkQjUkjFCFWyh13tkdDtNnJC-wEdtMSDoA8mr9yTT9pa7Km59xXmH2P79zbEuwWEzJareT_TLM4OQLfHpBMPgVxB7idVU4VM8rKnx2WSUztcpcJA_UNhr9rk4uzvOTCkHJCvzbH50OD_qlUELfM1lP-0ZHEVRgaKKqfRMCpbNGViM3yeMIDtvg2jrB41UrDEoVuE9mS9JllDF6z1ZhdnI1wTWoiO6PM5cck8i4b6VxUQUtHZPInQiDHmw9wWevOz4Mm_2IgbEd2pbQtgXtHvwiiJ-7Epl1bkjY2aIbVmkn0oRmirjrvBMOHdfMN8y7po5e9mCF8H7xvg7qHqw_ScwWzbu1rKnJ5GNcfPvPbRvw8eD06NAe_hn__Q4LNNwuprIOs9ObO_wB8_5-enF78zNPr0eBcsu-
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%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning+Model+for+Analyzing+EEG+Signal+Analysis&rft.jtitle=IEEE+access&rft.au=Gupta%2C+Varun&rft.au=Kumar%2C+Vivek&rft.au=Prince&rft.au=Singh%2C+Saurabh&rft.date=2025&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=13&rft.spage=91034&rft.epage=91045&rft_id=info:doi/10.1109%2FACCESS.2025.3563760&rft.externalDocID=10974958
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon