CNN and LSTM-Based Emotion Charting Using Physiological Signals

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 16; S. 4551
Hauptverfasser:	Dar, Muhammad Najam, Akram, Muhammad Usman, Khawaja, Sajid Gul, Pujari, Amit N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 14.08.2020 MDPI
Schlagworte:	Algorithms Arousal Classification convolutional neural network (CNN) Datasets ECG EEG Electrocardiography Electroencephalography emotion recognition Emotions Eye movements Galvanic Skin Response GSR Humans long short-term memory (LSTM) Neural networks Neural Networks, Computer Physiology Support vector machines Trends Wavelet transforms convolutional neural network (CNN) physiological signals EEG GSR ECG long short-term memory (LSTM) emotion recognition deep neural network
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.
AbstractList	Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments. Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence-High Arousal, High Valence-Low Arousal, Low Valence-High Arousal, and Low Valence-Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence-High Arousal, High Valence-Low Arousal, Low Valence-High Arousal, and Low Valence-Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments. Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of98.73%is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.
Author	Khawaja, Sajid Gul Pujari, Amit N. Dar, Muhammad Najam Akram, Muhammad Usman
AuthorAffiliation	3 School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK 1 Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; usman.akram@ceme.nust.edu.pk (M.U.A.); sajid.gul@ceme.nust.edu.pk (S.G.K.) 2 School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, England, UK; amit.pujari@ieee.org
AuthorAffiliation_xml	– name: 2 School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, England, UK; amit.pujari@ieee.org – name: 3 School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK – name: 1 Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; usman.akram@ceme.nust.edu.pk (M.U.A.); sajid.gul@ceme.nust.edu.pk (S.G.K.)
Author_xml	– sequence: 1 givenname: Muhammad Najam surname: Dar fullname: Dar, Muhammad Najam – sequence: 2 givenname: Muhammad Usman orcidid: 0000-0002-6208-7231 surname: Akram fullname: Akram, Muhammad Usman – sequence: 3 givenname: Sajid Gul surname: Khawaja fullname: Khawaja, Sajid Gul – sequence: 4 givenname: Amit N. orcidid: 0000-0003-1688-4448 surname: Pujari fullname: Pujari, Amit N.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32823807$$D View this record in MEDLINE/PubMed
BookMark	eNptkt9rFDEQx4O02F8--A_Igi_6sDabSXaTF0WPqoVrFXo-h7kku5djL6nJntD_3l2vHm0pgcyQfObLN5k5IQchBkfI64p-AFD0PDNa1VyI6gU5rjjjpWSMHjzIj8hJzmtKGQDIl-QImGQgaXNMPs2urwsMtpjfLK7KL5idLS42cfAxFLMVpsGHrviVp_3n6i772MfOG-yLG98F7PMZOWzH4F7dx1Oy-HqxmH0v5z--Xc4-z0vDazWUFihSxnkt2lZy2VbApXTAHTZcITcKLQLUKCijTjkhXIsC1bSWS2zglFzuZG3Etb5NfoPpTkf0-t9BTJ2evJreaaWYHWuFAqe4ZBKNs0JibaUQ0kg7an3cad1ulxtnjQtDwv6R6OOb4Fe6i390wxtGpRgF3t0LpPh76_KgNz4b1_cYXNxmzTjUoBhVE_r2CbqO2zR93EQJKnkjYKTePHS0t_K_TSPwfgeYFHNOrt0jFdXTCOj9CIzs-RPW-AGnho6P8f0zFX8BIbiwGw
CitedBy_id	crossref_primary_10_1109_JIOT_2024_3430297 crossref_primary_10_3390_app13084952 crossref_primary_10_1016_j_bspc_2022_103580 crossref_primary_10_26599_IJCS_2024_9100022 crossref_primary_10_1109_TSMC_2024_3523342 crossref_primary_10_1177_23780231211064009 crossref_primary_10_3390_s23104635 crossref_primary_10_1109_TCSS_2024_3406988 crossref_primary_10_1049_ccs2_12107 crossref_primary_10_1161_JAHA_123_034154 crossref_primary_10_1016_j_cmpb_2025_108921 crossref_primary_10_3233_JIFS_212958 crossref_primary_10_1016_j_icte_2023_09_001 crossref_primary_10_3390_sci6010010 crossref_primary_10_1038_s41598_022_18257_x crossref_primary_10_3390_app12094236 crossref_primary_10_1109_JSEN_2021_3073888 crossref_primary_10_3390_s23208636 crossref_primary_10_3390_electronics11030496 crossref_primary_10_1109_TNNLS_2022_3190448 crossref_primary_10_1109_ACCESS_2022_3219844 crossref_primary_10_1016_j_neucom_2023_126901 crossref_primary_10_1007_s10586_023_04133_4 crossref_primary_10_1016_j_bspc_2024_107039 crossref_primary_10_1016_j_engappai_2023_107391 crossref_primary_10_1002_widm_1563 crossref_primary_10_3390_s25041180 crossref_primary_10_1007_s00371_022_02502_5 crossref_primary_10_3390_brainsci13060947 crossref_primary_10_1016_j_inffus_2023_102019 crossref_primary_10_3389_fnins_2024_1519970 crossref_primary_10_1016_j_bspc_2024_106353 crossref_primary_10_1109_ACCESS_2024_3463746 crossref_primary_10_1109_JPROC_2023_3286445 crossref_primary_10_1016_j_compbiomed_2022_105327 crossref_primary_10_1016_j_cviu_2024_104121 crossref_primary_10_3390_s24092672 crossref_primary_10_3390_s22239480 crossref_primary_10_1016_j_eswa_2023_120883 crossref_primary_10_3389_fnhum_2022_1051463 crossref_primary_10_1016_j_bspc_2023_104928 crossref_primary_10_3390_electronics12132795 crossref_primary_10_1007_s11042_024_19467_3 crossref_primary_10_1109_ACCESS_2023_3321659 crossref_primary_10_1016_j_patrec_2022_08_018 crossref_primary_10_1016_j_ssci_2022_106019 crossref_primary_10_3390_fi16050158 crossref_primary_10_4015_S1016237224500510 crossref_primary_10_1080_10941665_2023_2293786 crossref_primary_10_1016_j_bspc_2025_107749 crossref_primary_10_1108_JEIM_12_2020_0536 crossref_primary_10_3390_mti6060047 crossref_primary_10_3390_s21031018 crossref_primary_10_1016_j_inffus_2022_03_009 crossref_primary_10_1155_2021_9951905
Cites_doi	10.1109/EMBC.2018.8512320 10.1109/JSEN.2010.2045498 10.3390/s18072074 10.1007/s00371-015-1183-y 10.1145/2973750.2973762 10.1109/T-AFFC.2011.28 10.1109/EMBC.2015.7320065 10.3390/s16101558 10.1016/j.bj.2017.11.001 10.1016/j.proeng.2011.08.452 10.1016/j.procs.2015.07.314 10.1109/BIBE.2016.40 10.1186/1475-925X-12-56 10.1109/T-AFFC.2010.7 10.1016/j.ipm.2019.102185 10.1109/JSTSP.2017.2764438 10.1007/978-981-10-7641-1_10 10.1109/T-AFFC.2011.25 10.3390/s20072034 10.1155/2017/8317357 10.1109/ICASSP40776.2020.9053985 10.1109/SMC.2014.6974415 10.5220/0006147801120118 10.1109/ICIP.2016.7532431 10.1109/TITS.2005.848368 10.1007/s11571-016-9375-3 10.3390/s19132999 10.1109/EMBC.2015.7319766 10.1037/h0077714 10.1007/978-3-642-22336-5_13 10.1089/tmj.2017.0250 10.1152/jn.90989.2008 10.1088/1741-2552/aa5a98 10.1166/jnsne.2014.1092 10.1109/ACCESS.2017.2779939 10.1109/CINC.2009.102 10.1109/SPAC.2017.8304293 10.1016/j.neucom.2019.10.008 10.1109/JBHI.2017.2688239 10.1016/j.neuroimage.2003.08.026 10.1109/T-AFFC.2010.1 10.1109/T-AFFC.2011.15 10.1109/BIBM.2016.7822545 10.1109/ICCV.2015.169 10.1016/j.bspc.2013.06.014 10.1109/ICASSP.2019.8683290 10.1109/TAFFC.2016.2625250 10.1109/ACII.2019.8925520 10.1109/ICBBE.2008.670
ContentType	Journal Article
Copyright	2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 by the authors. 2020
Copyright_xml	– notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 by the authors. 2020
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI 7X8 5PM DOA
DOI	10.3390/s20164551
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1424-8220
ExternalDocumentID	oai_doaj_org_article_992da5a593e94828aced58a6d8558c8d PMC7472085 32823807 10_3390_s20164551
Genre	Journal Article
GroupedDBID	--- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ALIPV ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI 7X8 5PM
ID	FETCH-LOGICAL-c469t-d30a024465ff848f13488e34ea749a4c9ada336a5020e9e55efa5a9a9a9bba73
IEDL.DBID	PIMPY
ISICitedReferencesCount	76
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000567263100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1424-8220
IngestDate	Fri Oct 03 12:42:28 EDT 2025 Tue Nov 04 02:00:48 EST 2025 Sun Nov 09 14:35:38 EST 2025 Tue Oct 07 07:26:50 EDT 2025 Wed Feb 19 02:01:59 EST 2025 Sat Nov 29 07:15:40 EST 2025 Tue Nov 18 22:41:52 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	16
Keywords	convolutional neural network (CNN) physiological signals EEG GSR ECG long short-term memory (LSTM) emotion recognition deep neural network
Language	English
License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c469t-d30a024465ff848f13488e34ea749a4c9ada336a5020e9e55efa5a9a9a9bba73
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-1688-4448 0000-0002-6208-7231
OpenAccessLink	https://www.proquest.com/publiccontent/docview/2435084753?pq-origsite=%requestingapplication%
PMID	32823807
PQID	2435084753
PQPubID	2032333
ParticipantIDs	doaj_primary_oai_doaj_org_article_992da5a593e94828aced58a6d8558c8d pubmedcentral_primary_oai_pubmedcentral_nih_gov_7472085 proquest_miscellaneous_2436392095 proquest_journals_2435084753 pubmed_primary_32823807 crossref_primary_10_3390_s20164551 crossref_citationtrail_10_3390_s20164551
PublicationCentury	2000
PublicationDate	20200814
PublicationDateYYYYMMDD	2020-08-14
PublicationDate_xml	– month: 8 year: 2020 text: 20200814 day: 14
PublicationDecade	2020
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Basel
PublicationTitle	Sensors (Basel, Switzerland)
PublicationTitleAlternate	Sensors (Basel)
PublicationYear	2020
Publisher	MDPI AG MDPI
Publisher_xml	– name: MDPI AG – name: MDPI
References	ref_14 ref_58 ref_13 ref_12 Bozhkov (ref_50) 2015; 53 ref_10 Koelstra (ref_54) 2011; 3 ref_19 ref_18 Soleymani (ref_55) 2011; 3 ref_15 Alhagry (ref_27) 2017; 8 ref_59 Russell (ref_11) 1980; 39 ref_61 ref_60 Petrantonakis (ref_16) 2010; 1 Calvo (ref_5) 2010; 1 Burns (ref_52) 2010; 10 Goshvarpour (ref_66) 2017; 40 Karim (ref_64) 2017; 6 Healey (ref_6) 2005; 6 ref_25 ref_24 ref_67 ref_22 ref_21 Okamoto (ref_63) 2004; 21 Yuvaraj (ref_8) 2016; 10 ref_20 ref_29 ref_28 ref_36 Ludwig (ref_62) 2009; 101 ref_34 ref_33 Hwang (ref_1) 2018; 24 ref_32 ref_31 Agrafioti (ref_7) 2011; 3 Zheng (ref_26) 2017; 14 ref_30 ref_39 Duvinage (ref_51) 2013; 12 ref_37 Subramanian (ref_57) 2016; 9 Xianhai (ref_23) 2011; 15 Ries (ref_53) 2014; 3 Basha (ref_65) 2020; 378 Singh (ref_56) 2013; 8 ref_47 ref_46 ref_44 ref_42 ref_41 ref_3 ref_2 Min (ref_43) 2017; 18 Lan (ref_17) 2016; 32 ref_49 ref_48 ref_9 Tzirakis (ref_45) 2017; 11 Katsigiannis (ref_35) 2017; 22 ref_4 Abdulhay (ref_38) 2018; 7 Li (ref_40) 2020; 57
References_xml	– ident: ref_41 doi: 10.1109/EMBC.2018.8512320 – ident: ref_32 – volume: 10 start-page: 1527 year: 2010 ident: ref_52 article-title: SHIMMER™–A wireless sensor platform for noninvasive biomedical research publication-title: IEEE Sensors J. doi: 10.1109/JSEN.2010.2045498 – ident: ref_9 doi: 10.3390/s18072074 – ident: ref_42 – volume: 32 start-page: 347 year: 2016 ident: ref_17 article-title: Real-time EEG-based emotion monitoring using stable features publication-title: Vis. Comput. doi: 10.1007/s00371-015-1183-y – ident: ref_4 doi: 10.1145/2973750.2973762 – volume: 3 start-page: 102 year: 2011 ident: ref_7 article-title: ECG pattern analysis for emotion detection publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2011.28 – ident: ref_20 doi: 10.1109/EMBC.2015.7320065 – ident: ref_59 doi: 10.3390/s16101558 – volume: 40 start-page: 355 year: 2017 ident: ref_66 article-title: An accurate emotion recognition system using ECG and GSR signals and matching pursuit method publication-title: Biomed. J. doi: 10.1016/j.bj.2017.11.001 – volume: 15 start-page: 2408 year: 2011 ident: ref_23 article-title: Study of emotion recognition based on electrocardiogram and RBF neural network publication-title: Procedia Eng. doi: 10.1016/j.proeng.2011.08.452 – ident: ref_31 – volume: 53 start-page: 375 year: 2015 ident: ref_50 article-title: EEG-based subject independent affective computing models publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.07.314 – ident: ref_21 doi: 10.1109/BIBE.2016.40 – volume: 12 start-page: 56 year: 2013 ident: ref_51 article-title: Performance of the Emotiv Epoc headset for P300-based applications publication-title: Biomed. Eng. Online doi: 10.1186/1475-925X-12-56 – volume: 1 start-page: 81 year: 2010 ident: ref_16 article-title: Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2010.7 – volume: 57 start-page: 102185 year: 2020 ident: ref_40 article-title: Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2019.102185 – ident: ref_13 – volume: 11 start-page: 1301 year: 2017 ident: ref_45 article-title: End-to-end multimodal emotion recognition using deep neural networks publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2017.2764438 – ident: ref_49 doi: 10.1007/978-981-10-7641-1_10 – volume: 3 start-page: 42 year: 2011 ident: ref_55 article-title: A multimodal database for affect recognition and implicit tagging publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2011.25 – ident: ref_61 doi: 10.3390/s20072034 – ident: ref_60 doi: 10.1155/2017/8317357 – ident: ref_39 doi: 10.1109/ICASSP40776.2020.9053985 – ident: ref_48 doi: 10.1109/SMC.2014.6974415 – ident: ref_28 – ident: ref_2 doi: 10.5220/0006147801120118 – ident: ref_30 – ident: ref_44 doi: 10.1109/ICIP.2016.7532431 – volume: 6 start-page: 156 year: 2005 ident: ref_6 article-title: Detecting stress during real-world driving tasks using physiological sensors publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2005.848368 – volume: 10 start-page: 225 year: 2016 ident: ref_8 article-title: Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients publication-title: Cogn. Neurodyn. doi: 10.1007/s11571-016-9375-3 – ident: ref_34 – ident: ref_46 doi: 10.3390/s19132999 – ident: ref_19 doi: 10.1109/EMBC.2015.7319766 – volume: 39 start-page: 1161 year: 1980 ident: ref_11 article-title: A circumplex model of affect publication-title: J. Personal. Soc. Psychol. doi: 10.1037/h0077714 – ident: ref_10 doi: 10.1007/978-3-642-22336-5_13 – volume: 24 start-page: 753 year: 2018 ident: ref_1 article-title: Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals publication-title: Telemed. e-Health doi: 10.1089/tmj.2017.0250 – volume: 101 start-page: 1679 year: 2009 ident: ref_62 article-title: Using a common average reference to improve cortical neuron recordings from microelectrode arrays publication-title: J. Neurophysiol. doi: 10.1152/jn.90989.2008 – volume: 14 start-page: 026017 year: 2017 ident: ref_26 article-title: A multimodal approach to estimating vigilance using EEG and forehead EOG publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aa5a98 – ident: ref_37 – ident: ref_14 – ident: ref_47 doi: 10.1109/SMC.2014.6974415 – volume: 3 start-page: 10 year: 2014 ident: ref_53 article-title: A comparison of electroencephalography signals acquired from conventional and mobile systems publication-title: J. Neurosci. Neuroeng. doi: 10.1166/jnsne.2014.1092 – volume: 6 start-page: 1662 year: 2017 ident: ref_64 article-title: LSTM fully convolutional networks for time series classification publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2779939 – ident: ref_18 doi: 10.1109/CINC.2009.102 – ident: ref_3 doi: 10.1109/SPAC.2017.8304293 – volume: 8 start-page: 355 year: 2017 ident: ref_27 article-title: Emotion recognition based on EEG using LSTM recurrent neural network publication-title: Emotion – ident: ref_25 – volume: 378 start-page: 112 year: 2020 ident: ref_65 article-title: Impact of fully connected layers on performance of convolutional neural networks for image classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.10.008 – ident: ref_29 – ident: ref_33 – volume: 22 start-page: 98 year: 2017 ident: ref_35 article-title: DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2017.2688239 – volume: 21 start-page: 99 year: 2004 ident: ref_63 article-title: Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping publication-title: Neuroimage doi: 10.1016/j.neuroimage.2003.08.026 – ident: ref_12 – volume: 1 start-page: 18 year: 2010 ident: ref_5 article-title: Affect detection: An interdisciplinary review of models, methods, and their applications publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2010.1 – volume: 3 start-page: 18 year: 2011 ident: ref_54 article-title: Deap: A database for emotion analysis; using physiological signals publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/T-AFFC.2011.15 – ident: ref_24 doi: 10.1109/BIBM.2016.7822545 – volume: 7 start-page: 57 year: 2018 ident: ref_38 article-title: Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS) publication-title: IEEE Access – ident: ref_15 – ident: ref_67 doi: 10.1109/ICCV.2015.169 – volume: 8 start-page: 740 year: 2013 ident: ref_56 article-title: A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2013.06.014 – ident: ref_36 doi: 10.1109/ICASSP.2019.8683290 – volume: 18 start-page: 851 year: 2017 ident: ref_43 article-title: Deep learning in bioinformatics publication-title: Briefings Bioinform. – volume: 9 start-page: 147 year: 2016 ident: ref_57 article-title: ASCERTAIN: Emotion and personality recognition using commercial sensors publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/TAFFC.2016.2625250 – ident: ref_58 doi: 10.1109/ACII.2019.8925520 – ident: ref_22 doi: 10.1109/ICBBE.2008.670
SSID	ssj0023338
Score	2.5931716
Snippet	Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and...
SourceID	doaj pubmedcentral proquest pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	4551
SubjectTerms	Algorithms Arousal Classification convolutional neural network (CNN) Datasets ECG EEG Electrocardiography Electroencephalography emotion recognition Emotions Eye movements Galvanic Skin Response GSR Humans long short-term memory (LSTM) Neural networks Neural Networks, Computer Physiology Support vector machines Trends Wavelet transforms
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NS-RAEC1EPOhB1vUrqyutePASnKS6J90nUVH2sDsIzsFbqKQ7OiBxccb9_VYlmTCzCF4kt6QOnXrprvdI8QrgNCBRUaZJXKAdxto7iouBdzFTe8SszKiyRTNsIhuN7MODu1sY9SU9Ya09cJu4c-dST4aMw-A0ywMqgzeWht4aY0vr5fRl1jMXU53UQlZerY8Qsqg_n6biJGVMslR9GpP-j5jl_w2SCxXn9htsdlRRXbZL3IKVUH-HjQUDwW24uB6NFNVe_b4f_4mvuCJ5ddMO5lHyI116mlXTFaCaVs_5SafuJ49inLwD49ub8fWvuBuJEJesY2exxwFxVdVDU1VW2ypB3oABdaBMO9KlI0-IQzLMAoMLxoSKk-fkKgrKcBdW65c67IMKGBKGR3NMpV3hLKNJSZYVKP2m1kVwNs9UXnZ24TK14jln2SBJzfukRnDSh_5tPTI-CrqSdPcBYmvd3GCw8w7s_DOwIzicg5V3e22ap8z4BlxkDUZw3D_mXSK_PqgOL29NDFOxlPlkBHsttv1KkFWn2O5HkC2hvrTU5Sf15Klx4mYtJjNOf3zFux3AeipaXux29SGszl7fwk9YK__NJtPXo-bzfgfnT_-W priority: 102 providerName: Directory of Open Access Journals
Title	CNN and LSTM-Based Emotion Charting Using Physiological Signals
URI	https://www.ncbi.nlm.nih.gov/pubmed/32823807 https://www.proquest.com/docview/2435084753 https://www.proquest.com/docview/2436392095 https://pubmed.ncbi.nlm.nih.gov/PMC7472085 https://doaj.org/article/992da5a593e94828aced58a6d8558c8d
Volume	20
WOSCitedRecordID	wos000567263100001&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: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 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: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwEB6xLQc48H4EliogDlysNrFd26cVXXUFEo0qtodyipzY2a2E0qXtcuS3M-OkYYtWnFAkH5I5TDJve_INwHvPrS3KNGEF12MmnLGsGDnDMLXnXJXKVroIwyZUlunl0szb36O3bVvl3icGR92gPVPfNjrhoVuXtGM-TDHKj9CxSn5y9YPRDCk6a20HahxBn4C3dA_688-z-beuAONYjzXoQhxL_eE2JXwpKZODmBSg-2_LN_9um7wRh84e_t83eAQP2nw0_tgo0GO44-sncP8GSuFTODnNstjWLv5yvpixCYY9F0-b6T8xndZT43QcWg_i0E-6d6fx-eqC0JmfweJsujj9xNq5C6zEYnnHHB9ZDN1iLKtKC10lHK3cc-GtEsaK0lhnOR9biammN15KX1lpDV1FYRV_Dr16XfuXEHvuE9QBgTSVMIXRqDI2Uarg1NSqTQQf9h8-L1tMchqN8T3H2oRklHcyiuBdR3rVAHHcRjQh6XUEhJ0dbqw3F3lrirkxqUN-peHeCCw4bemd1HbstJS61C6C47388tagt_kfcUXwtnuMpkjnK7b26-tAg_leiklrBC8aVek44VjaErZ_BOpAiQ5YPXxSry4D3DcWfDRI9dW_2XoN91LaCiC0XnEMvd3m2r-Bu-XP3Wq7GcCRWqqw6gH0J9Ns_nUQth9wnf2aDlpL-Q3hjx_l
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQceD8CBQwCqRerm9he2wdU0dKqVbcRUvewt8iJnbISypbdLYgfxX9kJq92UcWtB5RbPIqc5PM8nMn3AbwPwrm8SGKeCzPk0lvH84G3HFN7IXShXWnyWmxCp6mZTOyXNfjd_QtDbZWdT6wdtZ8VtEe-lWBcH6ArVWL77Dsn1Sj6utpJaDSwOAq_fmLJtvh4-Bnf74ck2d8b7x7wVlWAF1gKLrkXA4eBSQ5VWRppylgghoOQwWlpnSys806IoVOYSAUblAqlU87SkedOC7zsDbiJblxTB5meXNR3Asu9hrxICDvYWiREX6VUvBLyamWAq9LZv7syL4W5_fv_2QN6APfafJp9ahbAQ1gL1SO4e4ll8TFs76Ypc5Vno5PxMd_BsO3ZXqNexKjbgBq_Wd06wep-2C4csJPpKbFLP4HxddzAU1ivZlV4DiyIECOGJdqU0ubWIORdrHUuqCnX2Ag2uzebFS2nOkl7fMuwtiIQZD0IInjXm541RCJXGe0QPHoD4v6uT8zmp1nrSjJrE4_zVVYEK7FgdkXwyrihN0qZwvgINjqAZK1DWmQX6IjgbT-MroS-D7kqzM5rG8xXE0y6I3jWYLGficDSnLQJItArKF2Z6upINf1a05VjwUpCsC_-Pa03cPtgfDzKRofp0Uu4k9C2BjEPyw1YX87Pwyu4VfxYThfz1_WiY5BdM4b_AIKgagE
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VghAcyrukFDAIpF6i3cT22j6gij5WVC1Rpe5hb5ETO2WlKlt2tyB-Wv9dZ_JqF1XcekC5JaPIjj_PI5l8H8Anz63N8jgKM64HoXDGhlnfmRBTe85Vrmyhs0psQiWJHo_N8Qpctv_CUFtl6xMrR-2mOb0j78UY1_voSiXvFU1bxPHecPv8Z0gKUvSltZXTqCFy6P_8xvJt_uVgD9f6cxwP90e738JGYSDMsSxchI73LQYpMZBFoYUuIo549lx4q4SxIjfWWc4HVmJS5Y2X0hdWWkNHllnF8bb34L7CWZFqhBpf13ocS7-ayIhz0-_NY6KykjJaCn-VSsBtqe3fHZo3Qt7wyX_8sJ7CWpNns6_1xngGK758Do9vsC--gO3dJGG2dOzoZPQ93MFw7th-rWrEqAuBGsJZ1VLBqj7ZNkywk8kpsU6_hNFdTOAVrJbT0r8G5rmPENsCbQphMqNxK9hIqYxTs642AWy1q5zmDdc6SX6cpVhzESDSDhABfOxMz2uCkduMdggqnQFxglcnprPTtHExqTGxw_FKw70RWEjb3Dup7cBpKXWuXQCbLVjSxlHN02ukBPChu4wuhr4b2dJPLyobzGNjTMYDWK9x2Y2EY8lOmgUBqCXELg11-Uo5-VHRmGMhSwKxG_8e1nt4iNBNjw6SwzfwKKa3HURILDZhdTG78G_hQf5rMZnP3lX7j0F6xxC-Aq1tcrU
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=CNN+and+LSTM-Based+Emotion+Charting+Using+Physiological+Signals&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Dar%2C+Muhammad+Najam&rft.au=Akram%2C+Muhammad+Usman&rft.au=Khawaja%2C+Sajid+Gul&rft.au=Pujari%2C+Amit+N&rft.date=2020-08-14&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=20&rft.issue=16&rft_id=info:doi/10.3390%2Fs20164551&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon