Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm
A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be classified based on the changes in μ and β rhythms and their spatial distributions. Multi-layer perceptron neural networks (MLP-NNs) are c...
Uloženo v:
| Vydáno v: | Machine intelligence research (Print) Ročník 17; číslo 1; s. 108 - 122 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Beijing
Springer Nature B.V
01.02.2020
|
| Témata: | |
| ISSN: | 2153-182X, 2153-1838 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be classified based on the changes in μ and β rhythms and their spatial distributions. Multi-layer perceptron neural networks (MLP-NNs) are commonly used for classification. Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently. Conventional methods for training NNs, such as gradient descent and recursive methods, have some disadvantages including low accuracy, slow convergence speed and trapping in local minimums. In this paper, in order to overcome these issues, the MLP-NN trained by a hybrid population-physics-based algorithm, the combination of particle swarm optimization and gravitational search algorithm (PSOGSA), is proposed for our classification problem. To show the advantages of using PSOGSA that trains NNs, this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA) and new versions of PSO. The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics. The results show that the proposed algorithm in most subjects of encephalography (EEG) dataset has very better or acceptable performance compared to others. |
|---|---|
| AbstractList | A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can be classified based on the changes in μ and β rhythms and their spatial distributions. Multi-layer perceptron neural networks (MLP-NNs) are commonly used for classification. Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently. Conventional methods for training NNs, such as gradient descent and recursive methods, have some disadvantages including low accuracy, slow convergence speed and trapping in local minimums. In this paper, in order to overcome these issues, the MLP-NN trained by a hybrid population-physics-based algorithm, the combination of particle swarm optimization and gravitational search algorithm (PSOGSA), is proposed for our classification problem. To show the advantages of using PSOGSA that trains NNs, this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA) and new versions of PSO. The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics. The results show that the proposed algorithm in most subjects of encephalography (EEG) dataset has very better or acceptable performance compared to others. |
| Author | Afrakhteh, Sajjad Khishe, Mohammad Mosavi, Mohammad-Reza Ayatollahi, Ahmad |
| Author_xml | – sequence: 1 givenname: Sajjad surname: Afrakhteh fullname: Afrakhteh, Sajjad – sequence: 2 givenname: Mohammad-Reza surname: Mosavi fullname: Mosavi, Mohammad-Reza – sequence: 3 givenname: Mohammad surname: Khishe fullname: Khishe, Mohammad – sequence: 4 givenname: Ahmad surname: Ayatollahi fullname: Ayatollahi, Ahmad |
| BookMark | eNo9j09LwzAchoNMcM59AG8Bz9Ekv7TNjmPMTRgquIG3kaTJllmbmrRIv731D56e9_DwwHuJRnWoLULXjN4ySou7xFgOQCiThLFMEjhDY84yIEyCHP1v_nqBpimdKKUAnOYzGKNmbkwXVWvxolIpeeeNan2ocXB4uVzhF3-oVZXwLvn6gB_t4FYD2s8Q3xLeRuVrW2Ld43Wvoy_xc2i66qdAmmOfvCFapcGYV4cQfXt8v0Lnbgja6R8naHe_3C7WZPO0eljMN8SAEC1RIDMunBCOG6ZlwTOghSopOGGAzpjRxkmlhmdaFxJUqZzQlhZCFrmjMoMJuvntNjF8dDa1-1Po4veXPZ8xmUsJuYQvNPlfTQ |
| CitedBy_id | crossref_primary_10_3390_su14137781 crossref_primary_10_1109_ACCESS_2023_3274704 crossref_primary_10_1515_bmt_2021_0025 crossref_primary_10_3390_make3040042 crossref_primary_10_1016_j_aei_2025_103510 crossref_primary_10_1007_s12530_019_09280_x crossref_primary_10_1007_s11633_020_1231_6 crossref_primary_10_1016_j_energy_2022_125259 crossref_primary_10_1002_ima_22913 crossref_primary_10_1016_j_jclepro_2022_132697 crossref_primary_10_1007_s12530_025_09705_w crossref_primary_10_1007_s00500_019_04515_0 crossref_primary_10_1007_s00773_022_00897_3 crossref_primary_10_1155_2022_3216400 crossref_primary_10_1007_s12530_024_09612_6 crossref_primary_10_1186_s13634_024_01180_w crossref_primary_10_1007_s11063_022_11068_1 crossref_primary_10_1016_j_compbiomed_2025_111023 crossref_primary_10_1007_s11517_023_02782_6 crossref_primary_10_1007_s40815_021_01195_7 crossref_primary_10_1088_1741_2552_ad7f8e crossref_primary_10_1016_j_eswa_2022_119206 crossref_primary_10_1007_s11633_019_1178_7 crossref_primary_10_1016_j_bspc_2025_107706 crossref_primary_10_1016_j_knosys_2025_113548 crossref_primary_10_3390_jpm12030455 crossref_primary_10_1007_s00500_021_05886_z crossref_primary_10_1016_j_jestch_2024_101684 crossref_primary_10_1007_s40747_024_01502_3 crossref_primary_10_1007_s11277_022_09625_x crossref_primary_10_1007_s11633_019_1197_4 crossref_primary_10_1016_j_neucom_2025_130603 |
| ContentType | Journal Article |
| Copyright | Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018. |
| Copyright_xml | – notice: Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018. |
| DBID | 8FE 8FG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI |
| DOI | 10.1007/s11633-018-1158-3 |
| DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Health Research Premium Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition |
| DatabaseTitle | Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2153-1838 |
| EndPage | 122 |
| GroupedDBID | 8FE 8FG AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c344t-a38524f44f2c1b8725307ad03f4c3091cbcf8aa215bb783adaf4be074876f0853 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 34 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000515330400008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2153-182X |
| IngestDate | Fri Nov 07 23:37:25 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c344t-a38524f44f2c1b8725307ad03f4c3091cbcf8aa215bb783adaf4be074876f0853 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2918688368 |
| PQPubID | 6623301 |
| PageCount | 15 |
| ParticipantIDs | proquest_journals_2918688368 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-02-01 |
| PublicationDateYYYYMMDD | 2020-02-01 |
| PublicationDate_xml | – month: 02 year: 2020 text: 2020-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Beijing |
| PublicationPlace_xml | – name: Beijing |
| PublicationTitle | Machine intelligence research (Print) |
| PublicationYear | 2020 |
| Publisher | Springer Nature B.V |
| Publisher_xml | – name: Springer Nature B.V |
| SSID | ssj0003320693 |
| Score | 2.3395944 |
| Snippet | A brain-computer interface (BCI) system is one of the most effective ways that translates brain signals into output commands. Different imagery activities can... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 108 |
| SubjectTerms | Algorithms Classification Convergence Electroencephalography Heuristic methods Human-computer interface Multilayer perceptrons Multilayers Neural networks Particle swarm optimization Recursive methods Search algorithms Signal classification Spatial distribution Training |
| Title | Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm |
| URI | https://www.proquest.com/docview/2918688368 |
| Volume | 17 |
| WOSCitedRecordID | wos000515330400008&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: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2153-1838 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0003320693 issn: 2153-182X databaseCode: P5Z dateStart: 20041001 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2153-1838 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0003320693 issn: 2153-182X databaseCode: K7- dateStart: 20041001 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2153-1838 dateEnd: 20241214 omitProxy: false ssIdentifier: ssj0003320693 issn: 2153-182X databaseCode: BENPR dateStart: 20041001 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LTwIxEG4UPHjxETU-kPTgtRHaso-TQbPIabNRTIgX0nZbJFFAdjHh3ztTFjyYePGyl81umj5mvpnOfB8hNznSlCsumeLWMilCyWKLzTkxsktZF_DQebGJME2j4TDOqoRbUZVVbmyiN9T5zGCO_JbHSOweiSC6m38yVI3C29VKQmOX1NscjDBeyoZsm2MRgrcCz7sLjk0wgNLDzcWm754DLILFRBBGtTsRE7_MsfcxvcP_ju6IHFToknbX2-GY7NjpCZl3jVkiIwT1CphYG-SXg84cTZJH-jwZI4ky9dUDFNk64Bfpujy8oAPUkLA51SvaX2F7F822ml9snRdh6Apz2n0fw5DKt49T8tJLBg99VgktMCOkLJkSUYdLJ6Xjpq2jkHfg5Ku8JZw0AgCF0cZFSsEkah3C0ubKSW0BfIApdYDZxBmpTWdTe04oV1rzUDsFwA5WPoQvcxVYGyuI3LiKL0hjM4Gj6rQUo5_Zu_z79RXZ5xjv-qrpBqmVi6W9Jnvmq5wUiyap3ydp9tT0mwCeWef1Gyi3uzQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LL0QxFD5hSNh4BPHWBcsGbec-FiKDYQQ3E0Yyu9H2tkiYwQwyf8pvdE5nLguJnYV10ybtOTmvnvN9AJs5wZRrobgWznElY8VTR8M5KaFLOR-J2AeyiTjLkmYzrY_ARzELQ22VhU0MhjrvWKqRb4uUgN0TGSX7T8-cWKPod7Wg0BioxZnrv2PK1t07PUL5bglxXG0c1viQVYBbqVSPa5mUhfJKeWF3TRKLMqq5znekV1ai97TG-kRrdIXGxHiPXHtlHHpatBseAxSJ547CmJIqKpdg7KCa1S-_qjpSip0oIP3ifskxeG8WX6lhXg-jH2pfwsRtt5xw-cMBBK92PP3f3mMGpobxM6sMFH4WRlx7Dp4q1r4S5gULHJ_U_RQUjnU8q1ZP2NX9LcFEs9AfwQiPBI_IBg3wXdYglgyXM9NntT4NsLH6F6sZH1R-ODn7nFUebvEJeneP83D9J5dcgFK703aLwIQ2RsTGawxdUbdj3JnryLlUY24qdLoEq4XAWkN70G19S2v59-UNmKg1Ls5b56fZ2QpMCsruQ4_4KpR6L69uDcbtW----7I-VD0GN38t3U8e9Bax |
| 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=Accurate+Classification+of+EEG+Signals+Using+Neural+Networks+Trained+by+Hybrid+Population-physic-based+Algorithm&rft.jtitle=Machine+intelligence+research+%28Print%29&rft.au=Afrakhteh%2C+Sajjad&rft.au=Mosavi%2C+Mohammad-Reza&rft.au=Khishe%2C+Mohammad&rft.au=Ayatollahi%2C+Ahmad&rft.date=2020-02-01&rft.pub=Springer+Nature+B.V&rft.issn=2153-182X&rft.eissn=2153-1838&rft.volume=17&rft.issue=1&rft.spage=108&rft.epage=122&rft_id=info:doi/10.1007%2Fs11633-018-1158-3 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2153-182X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2153-182X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2153-182X&client=summon |