Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network
Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals ar...
Gespeichert in:
| Veröffentlicht in: | Electronics letters Jg. 56; H. 25; S. 1359 - 1361 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
The Institution of Engineering and Technology
10.12.2020
|
| Schlagworte: | |
| ISSN: | 0013-5194, 1350-911X, 1350-911X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals are one such source to capture and study human emotions. In this Letter, a novel time-order representation based on the S-transform and convolutional neural network (CNN) is proposed for the identification of human emotions. EEG signals are transformed into time-order representation (TOR) based on the S-transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state-of-the-art on the same dataset. |
|---|---|
| AbstractList | Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals are one such source to capture and study human emotions. In this Letter, a novel time-order representation based on the S-transform and convolutional neural network (CNN) is proposed for the identification of human emotions. EEG signals are transformed into time-order representation (TOR) based on the S-transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state-of-the-art on the same dataset. |
| Author | Upadhyay, A Khare, S.K Nishad, A Bajaj, V |
| Author_xml | – sequence: 1 givenname: S.K orcidid: 0000-0001-8365-1092 surname: Khare fullname: Khare, S.K organization: PDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, MP 482005, India – sequence: 2 givenname: A surname: Nishad fullname: Nishad, A organization: BITS Pilani, KK Birla Goa Campus, Goa 403726, India – sequence: 3 givenname: A surname: Upadhyay fullname: Upadhyay, A email: abhyragav24@gmail.com organization: Institute of Engineering and Technology, Bundelkhand University, Jhansi, UP 284128, India – sequence: 4 givenname: V orcidid: 0000-0002-6832-0495 surname: Bajaj fullname: Bajaj, V organization: PDPM-Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, MP 482005, India |
| BookMark | eNp9UEFOwzAQtFCRKKU3HuAjB1Ls2GmaI1RpQYrEAZC4Ra6zLobEruyEqg_g3ziEEwJOuxrN7MzOKRoZawChc0pmlPDsCupZTGIyi9mCHKExZQmJMkqfR2hMCGVRQjN-gqbe6w2hnPI54XSMPpa1CJDSUrTaGmwVhsb2q8fK2Qbn-Rp7vTWi9rjz2mxxqxuIrKvAYQc7Bx5MO4g3wkOFw9K-AH6IWieMV9Y1WJgKS2vebd31RFFjA537Gu3eurczdKyCAUy_5wQ9rfLH5W1U3K_vltdFJFkIHIESFCCl1UJkiSQp44kQqcykrKSgUrDwo1IBI2qheDXnKaMpr2JWURHHfM4mKB7uSme9d6BKqYfsIauuS0rKvssS6rLvsuy7DKLLH6Kd041wh7_oyUDf6xoO_3LLvCjim1V4JOmzXQw6DW35ajvXV_67xSe_OJe3 |
| CitedBy_id | crossref_primary_10_1109_JSEN_2021_3121293 crossref_primary_10_1016_j_bspc_2024_106074 crossref_primary_10_1016_j_heliyon_2024_e38681 crossref_primary_10_1016_j_bspc_2024_106590 crossref_primary_10_1016_j_bspc_2024_106974 crossref_primary_10_1016_j_cmpb_2023_107865 crossref_primary_10_1109_JSEN_2023_3239507 crossref_primary_10_1016_j_bspc_2023_105821 crossref_primary_10_1016_j_bspc_2024_107369 crossref_primary_10_1109_JSEN_2023_3304891 crossref_primary_10_1016_j_bspc_2023_105223 crossref_primary_10_1002_ima_22913 crossref_primary_10_1016_j_bspc_2024_106929 crossref_primary_10_1016_j_bspc_2023_105933 crossref_primary_10_1016_j_inffus_2023_101898 crossref_primary_10_1016_j_jksuci_2021_08_021 crossref_primary_10_1049_sil2_12076 crossref_primary_10_1109_ACCESS_2025_3592729 crossref_primary_10_1016_j_bspc_2024_106241 crossref_primary_10_3390_bioengineering10101200 crossref_primary_10_1007_s12652_023_04715_5 crossref_primary_10_1016_j_bspc_2025_108046 crossref_primary_10_1016_j_apacoust_2023_109620 crossref_primary_10_3390_s22218128 crossref_primary_10_1016_j_bspc_2024_106447 crossref_primary_10_1007_s11571_024_10198_7 crossref_primary_10_1016_j_bspc_2024_106446 crossref_primary_10_1016_j_bspc_2024_107018 crossref_primary_10_1016_j_bspc_2024_106624 crossref_primary_10_1016_j_bspc_2024_106344 crossref_primary_10_1016_j_bspc_2024_106489 crossref_primary_10_1016_j_bspc_2025_107555 crossref_primary_10_1016_j_bspc_2023_105926 crossref_primary_10_3390_bdcc6010016 crossref_primary_10_1186_s12859_023_05544_1 crossref_primary_10_3390_app13137908 crossref_primary_10_3390_info15060301 crossref_primary_10_1016_j_compbiomed_2023_107374 crossref_primary_10_1016_j_inffus_2023_102019 crossref_primary_10_1109_JSEN_2022_3202209 crossref_primary_10_1007_s13755_023_00224_z crossref_primary_10_3390_s23187860 |
| Cites_doi | 10.1109/JSEN.2019.2962874 10.1016/j.procs.2017.05.025 10.1109/78.492555 10.1109/TAFFC.2017.2660485 10.1109/TBME.2010.2048568 10.1109/TASLP.2014.2335056 10.1016/j.dsp.2012.05.007 10.1109/ACCESS.2020.3006082 10.1049/iet-smt.2018.5237 10.1007/s13755-018-0048-y 10.1016/j.cmpb.2019.03.015 10.4236/jbise.2010.34054 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2020 The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2020 The Institution of Engineering and Technology |
| DBID | AAYXX CITATION |
| DOI | 10.1049/el.2020.2380 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1350-911X |
| EndPage | 1361 |
| ExternalDocumentID | 10_1049_el_2020_2380 ELL2BF07356 |
| Genre | rapidPublication |
| GroupedDBID | 0R 24P 29G 4IJ 5GY 6IK 8VB AAJGR ABPTK ABZEH ACGFS ACIWK AENEX ALMA_UNASSIGNED_HOLDINGS BFFAM CS3 DU5 ESX F5P HZ IFIPE IPLJI JAVBF KBT LAI LOTEE LXI LXO LXU M43 MS NADUK NXXTH O9- OCL P2P QWB RIE RNS RUI TN5 U5U UNMZH UNR WH7 X ZL0 ZZ -4A -~X .DC 0R~ 0ZK 1OC 2QL 3EH 4.4 8FE 8FG 96U AAHHS AAHJG ABJCF ABQXS ACCFJ ACCMX ACESK ACGFO ACXQS ADEYR ADIYS ADZOD AEEZP AEGXH AEQDE AFAZI AFKRA AI. AIAGR AIWBW AJBDE ALUQN ARAPS AVUZU BBWZM BENPR BGLVJ CCPQU EBS EJD ELQJU F8P GOZPB GROUPED_DOAJ GRPMH HCIFZ HZ~ IAO IFBGX ITC K1G K7- L6V M7S MCNEO MS~ OK1 P0- P62 PTHSS R4Z RIG VH1 ~ZZ AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c3146-efa1ee71d8a95c07345aa7c9ccdca1ca3519ff5aa0f8f4d6473174d23d1a22463 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 45 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000604957700002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0013-5194 1350-911X |
| IngestDate | Wed Oct 29 21:10:08 EDT 2025 Tue Nov 18 21:06:23 EST 2025 Wed Jan 22 16:31:52 EST 2025 Wed Jan 06 04:41:12 EST 2021 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 25 |
| Keywords | electroencephalography CNN brain-computer interfaces affective computing convolutional neural network time-order representation electroencephalogram signals transforms brain–computer interface deep features emotion recognition behaviour conditions medical signal processing cognition conditions human emotions medical conditions TOR information source medical diagnosis system EEG signals convolutional neural nets |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3146-efa1ee71d8a95c07345aa7c9ccdca1ca3519ff5aa0f8f4d6473174d23d1a22463 |
| ORCID | 0000-0002-6832-0495 0000-0001-8365-1092 |
| PageCount | 3 |
| ParticipantIDs | crossref_primary_10_1049_el_2020_2380 iet_journals_10_1049_el_2020_2380 crossref_citationtrail_10_1049_el_2020_2380 wiley_primary_10_1049_el_2020_2380_ELL2BF07356 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | 2020-12-10 |
| PublicationDateYYYYMMDD | 2020-12-10 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-12-10 day: 10 |
| PublicationDecade | 2020 |
| PublicationTitle | Electronics letters |
| PublicationYear | 2020 |
| Publisher | The Institution of Engineering and Technology |
| Publisher_xml | – name: The Institution of Engineering and Technology |
| References | Pooja, Jain; Pachori, Ram Bilas (C15) 2014; 22 Smith, K.; Varun, B. (C12) 2020 Murugappan, N.; Ramachandran, M.; Sazali, Y. (C7) 2010; 334054 Silvia, U.L.; Smith, K.; Varun, B. (C16) 2020; 8 Liu, Y.; Yu, M.; Zhao, G. (C4) 2018; 9 Lin, Y.; Wang, C.; Jung, T. (C6) 2010; 57 Chen, L.; Mao, X.; Xue, Y. (C3) 2012; 22 Sawangjai, P.; Hompoonsup, S.; Leelaarporn, P. (C1) 2020; 20 Varun, B.; Sachin, T.; Abdulkadir, S. (C10) 2018; 6 Zhuang, N.; Zeng, Y.; Tong, L. (C8) 2017; 01 Stockwell, R.G.; Mansinha, L.; Lowe, R.P. (C14) 1996; 44 Sachin, T.; Varun, B. (C11) 2019; 173 Smith, K.; Varun, B.; Sinha, G.R. (C13) 2020 Varun, B.; Annala, K.H.; Sri, A.B. (C9) 2019; 13 2020; 8 2018; 6 2018; 9 2010; 57 2020; 20 2020 7062 2019; 13 2010; 334054 2017; 01 2017 2019; 173 2012; 22 2014; 22 1996; 44 e_1_2_5_16_2 e_1_2_5_8_2 e_1_2_5_15_2 Wang X.‐W. (e_1_2_5_6_2) e_1_2_5_7_2 e_1_2_5_10_2 e_1_2_5_5_2 e_1_2_5_12_2 e_1_2_5_4_2 e_1_2_5_11_2 e_1_2_5_3_2 e_1_2_5_2_2 Zhuang N. (e_1_2_5_9_2) 2017; 01 Smith K. (e_1_2_5_14_2) 2020 e_1_2_5_18_2 e_1_2_5_17_2 Smith K. (e_1_2_5_13_2) 2020 |
| References_xml | – volume: 57 start-page: 1798 issue: 7 year: 2010 end-page: 1806 ident: C6 article-title: EEG-based emotion recognition in music listening publication-title: IEEE Trans. Biomed. Eng. – volume: 22 start-page: 1467 year: 2014 end-page: 1482 ident: C15 article-title: Event-based method for instantaneous fundamental frequency estimation from voiced speech based on eigenvalue decomposition of the Hankel matrix publication-title: IEEE/ACM Trans. Audio Speech and Lang. Process. – year: 2020 ident: C12 article-title: Time-frequency representation and convolutional neural network based emotion recognition publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 01 start-page: 1 year: 2017 end-page: 9 ident: C8 article-title: Emotion recognition from EEG signals using multidimensional information in EMD domain publication-title: BioMed Res. Int. – volume: 20 start-page: 3996 issue: 8 year: 2020 end-page: 4024 ident: C1 article-title: Consumer grade EEG measuring sensors as research tools: a review publication-title: IEEE Sens. J. – volume: 22 start-page: 1154 issue: 6 year: 2012 end-page: 1160 ident: C3 article-title: Speech emotion recognition: features and classification models publication-title: Digit. Signal Process. – volume: 334054 start-page: 390 year: 2010 end-page: 396 ident: C7 article-title: Classification of human emotion from EEG using discrete wavelet transform publication-title: J. Biomed. Sci. Eng. – volume: 173 start-page: 157 year: 2019 end-page: 165 ident: C11 article-title: Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method publication-title: Comput. Methods Programs Biomed. – volume: 6 start-page: 12 issue: 1 year: 2018 ident: C10 article-title: Emotion classification using flexible analytic wavelet transform for electroencephalogram signals publication-title: Health Inf. Sci. Syst. – volume: 9 start-page: 550 issue: 4 year: 2018 end-page: 0562 ident: C4 article-title: Real-time movieinduced discrete emotion recognition from EEG signals publication-title: IEEE Trans. Affective Comput. – volume: 13 start-page: 375 issue: 3 year: 2019 end-page: 380 ident: C9 article-title: Emotion classification using EEG signals based on tunable-Q wavelet transform publication-title: IET Sci., Meas. Technol. – year: 2020 ident: C13 article-title: Adaptive tunable Q wavelet transform based emotion identification publication-title: IEEE Trans. Instrum. Meas. – volume: 44 start-page: 998 year: 1996 end-page: 1001 ident: C14 article-title: Localization of the complex spectrum: the S transform publication-title: IEEE Trans. Signal Process. – volume: 8 start-page: 124055 year: 2020 end-page: 124065 ident: C16 article-title: Hybrid computerized method for environmental sound classification publication-title: IEEE Access – volume: 01 start-page: 1 year: 2017 end-page: 9 article-title: Emotion recognition from EEG signals using multidimensional information in EMD domain publication-title: BioMed Res. Int. – volume: 44 start-page: 998 year: 1996 end-page: 1001 article-title: Localization of the complex spectrum: the S transform publication-title: IEEE Trans. Signal Process. – volume: 57 start-page: 1798 issue: 7 year: 2010 end-page: 1806 article-title: EEG‐based emotion recognition in music listening publication-title: IEEE Trans. Biomed. Eng. – year: 2020 article-title: Adaptive tunable Q wavelet transform based emotion identification publication-title: IEEE Trans. Instrum. Meas. – volume: 22 start-page: 1154 issue: 6 year: 2012 end-page: 1160 article-title: Speech emotion recognition: features and classification models publication-title: Digit. Signal Process. – volume: 22 start-page: 1467 year: 2014 end-page: 1482 article-title: Event‐based method for instantaneous fundamental frequency estimation from voiced speech based on eigenvalue decomposition of the Hankel matrix publication-title: IEEE/ACM Trans. Audio Speech and Lang. Process. – start-page: 1175 year: 2017 end-page: 1184 article-title: Emotion recognition using facial expressions – volume: 9 start-page: 550 issue: 4 year: 2018 end-page: 0562 article-title: Real‐time movieinduced discrete emotion recognition from EEG signals publication-title: IEEE Trans. Affective Comput. – volume: 334054 start-page: 390 year: 2010 end-page: 396 article-title: Classification of human emotion from EEG using discrete wavelet transform publication-title: J. Biomed. Sci. Eng. – volume: 13 start-page: 375 issue: 3 year: 2019 end-page: 380 article-title: Emotion classification using EEG signals based on tunable‐Q wavelet transform publication-title: IET Sci., Meas. Technol. – volume: 8 start-page: 124055 year: 2020 end-page: 124065 article-title: Hybrid computerized method for environmental sound classification publication-title: IEEE Access – volume: 20 start-page: 3996 issue: 8 year: 2020 end-page: 4024 article-title: Consumer grade EEG measuring sensors as research tools: a review publication-title: IEEE Sens. J. – volume: 173 start-page: 157 year: 2019 end-page: 165 article-title: Emotion recognition from single‐channel EEG signals using a two‐stage correlation and instantaneous frequency‐based filtering method publication-title: Comput. Methods Programs Biomed. – year: 2020 article-title: Time‐frequency representation and convolutional neural network based emotion recognition publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 7062 – volume: 6 start-page: 12 issue: 1 year: 2018 article-title: Emotion classification using flexible analytic wavelet transform for electroencephalogram signals publication-title: Health Inf. Sci. Syst. – ident: e_1_2_5_2_2 doi: 10.1109/JSEN.2019.2962874 – ident: e_1_2_5_3_2 doi: 10.1016/j.procs.2017.05.025 – year: 2020 ident: e_1_2_5_14_2 article-title: Adaptive tunable Q wavelet transform based emotion identification publication-title: IEEE Trans. Instrum. Meas. – ident: e_1_2_5_15_2 doi: 10.1109/78.492555 – year: 2020 ident: e_1_2_5_13_2 article-title: Time‐frequency representation and convolutional neural network based emotion recognition publication-title: IEEE Trans. Neural Netw. Learn. Syst. – ident: e_1_2_5_18_2 – ident: e_1_2_5_5_2 doi: 10.1109/TAFFC.2017.2660485 – ident: e_1_2_5_7_2 doi: 10.1109/TBME.2010.2048568 – ident: e_1_2_5_16_2 doi: 10.1109/TASLP.2014.2335056 – ident: e_1_2_5_4_2 doi: 10.1016/j.dsp.2012.05.007 – volume-title: Neural Information Processing ident: e_1_2_5_6_2 – volume: 01 start-page: 1 year: 2017 ident: e_1_2_5_9_2 article-title: Emotion recognition from EEG signals using multidimensional information in EMD domain publication-title: BioMed Res. Int. – ident: e_1_2_5_17_2 doi: 10.1109/ACCESS.2020.3006082 – ident: e_1_2_5_10_2 doi: 10.1049/iet-smt.2018.5237 – ident: e_1_2_5_11_2 doi: 10.1007/s13755-018-0048-y – ident: e_1_2_5_12_2 doi: 10.1016/j.cmpb.2019.03.015 – ident: e_1_2_5_8_2 doi: 10.4236/jbise.2010.34054 |
| SSID | ssib014146041 ssj0012997 |
| Score | 2.5181952 |
| Snippet | Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions... |
| SourceID | crossref wiley iet |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1359 |
| SubjectTerms | affective computing behaviour conditions brain‐computer interfaces brain–computer interface CNN cognition conditions convolutional neural nets convolutional neural network deep features EEG signals electroencephalogram signals electroencephalography emotion recognition human emotions information source medical conditions medical diagnosis system medical signal processing Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications time‐order representation TOR transforms |
| Title | Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network |
| URI | http://digital-library.theiet.org/content/journals/10.1049/el.2020.2380 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fel.2020.2380 |
| Volume | 56 |
| WOSCitedRecordID | wos000604957700002&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: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1350-911X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012997 issn: 0013-5194 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley-Blackwell Open Access Collection customDbUrl: eissn: 1350-911X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012997 issn: 0013-5194 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFA86PejBb3F-EUFPUlzaZG2PKp0KYwxU3K2k-ZDB6GSrnr1682_0L_G9tBvzMEG8tCUkJeTlfST55fcIOfUzZY31Mw-WyMYDD8E8iEKEx02GiCEtmXYkru2w04l6vbhbbbjhXZiSH2K64Yaa4ew1KrjMyiwkENSiEPHgwEckcwRL9iXG4I3Mzrw7PUUAU-uSqwSigUrdq4Dv0P5itvUPl7TYN8XPQNV5mtb6f_u4QdaqGJNelpNikyyYfIuszjAPbpMPlwwTYUJOMnRoqSkT-owp3jihSXJDEdsBs5MiNv6ZYhb6r_dPx9VJHRfm5N5STtEXagofEE7Se6hVTOJhKnNNEdpeTXHoF1JoupcDoO-Qx1bycH3rVVkZPBWAWfWMlcyYkOlIxkKBheBCylDFSmklmZKY8c9aKGvYyHLd5CGEKFz7gWYS2euCXVLLh7nZI9RqsDBK-SLKDJdCx6oplOImzvw44szWyflEMKmqKMsxc8YgdUfnPE7NIMUBTnGA6-RsWvulpOqYU-8EZJxWujqeU6eU668_SpN2279qwRCI5v5fGxyQFSxHZAxrHJJaMXo1R2RZvRX98ejYzWN4Pt11vgGmR_eo |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFA86BfXgtzg_I-hJimubru1RZXPDWgQn7lbSfMhgdLJVz169-Tf6l_he2o15mCCeWspLCXnJy0vyy-9HyKmTCq20k1qwRFYWzBC2BVmIZzGVImJIclsaEtfIj-Og2w3vS51TvAtT8ENMNtxwZJh4jQMcN6SLBSdDkkyFJwcOQpkDWLMvMMg1ULvhqR1PjhEg1hp1Fder4ajulsh3KH8xXfrHnDTfU_nPTNVMNc21f1dynayWWSa9LLrFBplT2SZZmeIe3CIfRg4TgULGN3SgqSokfUYU75zQRuOGIroD-idFdPwzRR36r_dPw9ZJDRvm-OZSRnE2lBReIKGkD2CVjzNiyjNJEdxednKoF5JomoeBoG-Tx2ajc92ySl0GS7gQWC2lua2Ub8uAh56AGME8zn0RCiEFtwVHzT-t4VtNB5rJOvMhSWHScaXNkb_O3SGVbJCpXUK1hBgjhOMFqWLck6Goe0IwFaZOGDBbV8n52DOJKEnLUTujn5jDcxYmqp9gAyfYwFVyNrF-Kcg6ZtidgJOTcrSOZtgUjv31R0kjipyrJjSBV9_7a4FjstTq3EVJ1I5v98ky2iBOxq4dkEo-fFWHZFG85b3R8Mh06m8qmPrx |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8NAFB60iujBXazrCHqSYCeZaZKjS6tiKQUVeguTWaRQ0tJWz169-Rv9Jb43SUs9VBBPCeFNCPPWyXzzPUJO_VRZY_3UgyWy8SBDMA-qEOFxkyJiSEumHYlrI2w2o3Y7bhV9TvEsTM4PMfnhhp7h4jU6uOlrmy84OZJkGtw58BHKHMGafYGLkKFZ-7w12UaAWOu6qwSigl7dLpDvMP5ievSPnDTfMaOflapLNfW1f3_kOlktqkx6mZvFBpkz2SZZmeIe3CIfrh0mAoWcbmjPUpO39BlSPHNCa7VbiugOsE-K6PgXin3ov94_HVsndWyY45NLGcVsqCncQEFJH0FqNK6Iqcw0RXB7YeTwXUii6S4Ogr5Nnuu1p-s7r-jL4KkAAqtnrGTGhExHMhYKYgQXUoYqVkoryZTEnn_WwrOKjSzXVR5CkcK1H2gmkb8u2CGlrJeZXUKthhijlC-i1HApdKyqQilu4tSPI85smZyPNZOogrQce2d0E7d5zuPEdBOc4AQnuEzOJtL9nKxjhtwJKDkpvHU4QyZX7K8vSmqNhn9VhykQ1b2_DjgmS62betK4bz7sk2UUQZgMqxyQ0mjwag7JonobdYaDI2fT3z_e-gg |
| 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=Classification+of+emotions+from+EEG+signals+using+time%E2%80%90order+representation+based+on+the+S%E2%80%90transform+and+convolutional+neural+network&rft.jtitle=Electronics+letters&rft.au=Khare%2C+S.K.&rft.au=Nishad%2C+A.&rft.au=Upadhyay%2C+A.&rft.au=Bajaj%2C+V.&rft.date=2020-12-10&rft.issn=0013-5194&rft.eissn=1350-911X&rft.volume=56&rft.issue=25&rft.spage=1359&rft.epage=1361&rft_id=info:doi/10.1049%2Fel.2020.2380&rft.externalDBID=n%2Fa&rft.externalDocID=10_1049_el_2020_2380 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0013-5194&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0013-5194&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0013-5194&client=summon |