Affective Computing Model Using Source-temporal Domain
This paper proposes a new Electroencephalographic (EEG) emotion recognition system (EEG-ER) that captures human emotion dynamics. EEG signals are collected from ten healthy subjects, aged 5-6 years. Four basic emotions namely; happy, sad, neutral and fear were induced from the participants using aff...
Saved in:
| Published in | Procedia, social and behavioral sciences Vol. 97; pp. 54 - 62 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
06.11.2013
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This paper proposes a new Electroencephalographic (EEG) emotion recognition system (EEG-ER) that captures human emotion dynamics. EEG signals are collected from ten healthy subjects, aged 5-6 years. Four basic emotions namely; happy, sad, neutral and fear were induced from the participants using affective photographs of varying arousal from the Radbound faces database (RaFD). The affective space model proposed by was used for classifying the acquired signals into a 2-dimensional structure of valence and arousal. Feature extraction method utilized was Time Difference of Arrival (TDOA) approach for reconstructing the relative source domain of brain activity. Regularized Least Square (RLS) and Multi-Layer Perception (MLP) neural network was used for classification process. The results were compared with wavelet coefficients (WC) method and showed high accuracy around 96% for user independent classification and approximately100% for user dependent classification. Overall the results reflect significant stability of accuracy rate among subjects using the proposed method. |
|---|---|
| AbstractList | This paper proposes a new Electroencephalographic (EEG) emotion recognition system (EEG-ER) that captures human emotion dynamics. EEG signals are collected from ten healthy subjects, aged 5-6 years. Four basic emotions namely; happy, sad, neutral and fear were induced from the participants using affective photographs of varying arousal from the Radbound faces database (RaFD). The affective space model proposed by was used for classifying the acquired signals into a 2-dimensional structure of valence and arousal. Feature extraction method utilized was Time Difference of Arrival (TDOA) approach for reconstructing the relative source domain of brain activity. Regularized Least Square (RLS) and Multi-Layer Perception (MLP) neural network was used for classification process. The results were compared with wavelet coefficients (WC) method and showed high accuracy around 96% for user independent classification and approximately100% for user dependent classification. Overall the results reflect significant stability of accuracy rate among subjects using the proposed method. |
| Author | Shams, Wafaa Khazaal Wahab, Abdul Fakhri, Imad |
| Author_xml | – sequence: 1 givenname: Wafaa Khazaal surname: Shams fullname: Shams, Wafaa Khazaal email: wafaa_dth@yahoo.com – sequence: 2 givenname: Abdul surname: Wahab fullname: Wahab, Abdul – sequence: 3 givenname: Imad surname: Fakhri fullname: Fakhri, Imad |
| BookMark | eNqFkMFKAzEQhoNUsNa-gYd9ga3JJptsPQilWhUqHrTnkE0mkrK7WZJtwbc3Sz2IBx0Y_mHg_4dvLtGk8x0gdE3wgmDCb_aLWMc--EWBCU2rpOwMTUklRI5ZUU1-zBdoHuMep6KELotiivjKWtCDO0K29m1_GFz3kb14A022i-P85g9BQz5A2_ugmuzet8p1V-jcqibC_FtnaLd5eF8_5dvXx-f1apvroiqGnBlmNC3TeUVIyTllQtWcEiUMs2VZW4IrziheGgyixpQbKkDYZT02FCWdIXbK1cHHGMDKPrhWhU9JsBzx5V6e8OWIP24TfrLd_rJpN6jB-W4IyjX_me9OZkhgRwdBRu2g02BcSK-Sxru_A74APXR58w |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2022_3227627 crossref_primary_10_1109_ACCESS_2020_2980893 |
| Cites_doi | 10.1016/j.patcog.2010.09.020 10.1016/S1071-5819(03)00052-1 10.1080/02699930903485076 10.1016/S1071-5819(03)00051-X 10.21236/ADA164453 10.7551/mitpress/9108.001.0001 10.1109/TAES.1972.309614 10.1016/S0031-3203(02)00052-3 10.1016/j.neulet.2005.09.004 10.1109/TASSP.1976.1162830 10.1016/j.clinph.2005.08.034 10.1109/34.954607 10.1037/h0077714 10.1109/ICSMC.2007.4413638 10.1109/TMM.2006.870737 10.1109/ICME.2005.1521560 10.1016/j.ijpsycho.2006.07.003 10.1007/978-3-642-34478-7_47 10.1090/S0002-9947-1950-0051437-7 |
| ContentType | Journal Article |
| Copyright | 2013 The Authors |
| Copyright_xml | – notice: 2013 The Authors |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.sbspro.2013.10.204 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Social Sciences (General) |
| EISSN | 1877-0428 |
| EndPage | 62 |
| ExternalDocumentID | 10_1016_j_sbspro_2013_10_204 S1877042813036495 |
| GroupedDBID | 0R~ 0SF 4.4 457 5VS 6I. 71M AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP2 EP3 F5P FDB FEDTE FNPLU HVGLF IXB KQ8 NCXOZ O-L O9- OK1 OZT ROL SES SSZ XH2 AAYWO AAYXX ADVLN CITATION HZ~ |
| ID | FETCH-LOGICAL-c282t-4d4dc35877a11566347ab631a7d4f55bf10864309d0e7b036d37e7f9b7f9be253 |
| IEDL.DBID | IXB |
| ISSN | 1877-0428 |
| IngestDate | Tue Jul 01 04:57:46 EDT 2025 Thu Apr 24 22:55:25 EDT 2025 Fri Feb 23 02:30:14 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | RafD faces 2 Dimension affective model emotion relative source temporal features EEG |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc-nd/3.0 https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c282t-4d4dc35877a11566347ab631a7d4f55bf10864309d0e7b036d37e7f9b7f9be253 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S1877042813036495 |
| PageCount | 9 |
| ParticipantIDs | crossref_primary_10_1016_j_sbspro_2013_10_204 crossref_citationtrail_10_1016_j_sbspro_2013_10_204 elsevier_sciencedirect_doi_10_1016_j_sbspro_2013_10_204 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2013-11-06 |
| PublicationDateYYYYMMDD | 2013-11-06 |
| PublicationDate_xml | – month: 11 year: 2013 text: 2013-11-06 day: 06 |
| PublicationDecade | 2010 |
| PublicationTitle | Procedia, social and behavioral sciences |
| PublicationYear | 2013 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Murugappan M, Rizon M , Nagarajan R , YaacobS , Zunaidi I , Hazry D. EEG feature extraction for classifying emotions using FCM and FKM. J Comput. Commun 2007; 1: 21-25. Atkinson K.E .An introduction to numerical analysis. John Wiley & Sons 1989. Chanel G, Ansari-Asl K, Pun T. Valence-arousal evaluation using physiological signals in an emotion recall paradigm. IEEE International Conference on Systems, Man and Cybernetics, ISIC 2007: 2662-2667. Picard (bib0035) 2003; 59 Langner O,Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, Knippenberg AV. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 2010; 24: 1377-1388. Tacchetti A, Mallapragada PS,Santoro M,RosascoL. GURLS: a Toolbox for Regularized Least Squares Learning. Computer Science and Artifical Intelligence Laboratory , Technical Report CBCL-306, MIT , 2012. Yeasin M, Bullot B,Sharma R. Recognition of facial expressions and measurement of levels of interest from video. IEEE Transactions on Multimedia 2006; 8: 500-508. Balconi M , Lucchiari C. EEG correlates (event-related desynchronization) of emotional face elaboration: a temporal analysis. Neuroscience Letters 2006; 392: 118-123. Rumelhart D,Hinton G, Williams R. Learning Internal Representations by Error Propagation. In Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1986. Schuller B, Reiter S, Muller R, Al-Hames M, Lang M, Rigoll G. Speaker independent speech emotion recognition by ensemble classification. IEEE International Conference on Multimedia and Expo, ICME 2005:864-867. Kayser J, Tenke CE. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: Evaluation with auditory oddball tasks. Clinical Neurophysiology 2006; 117: 348-368. Cohen I, Garg, Huang TS. Emotion recognition from facial expressions using multilevel HMM. Neural information processing systems 2000. Citeseer. Güntekin B, Basar E. Emotional face expressions are differentiated with brain oscillations. International Journal of Psychophysiology2007; 64: 91-100. Aronszajn N. Theory of reproducing kernels. Trans. Amer. Math. Soc1950; 68: 337-404. EIAyadi M, Kamel MS, KarrayF. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 2011; 44: 572-587. Russell JA. A circumplex model of affect. Journal of personality and social psychology, 1980; 39: 1161-1178. Shams WK., Wahab W, Qidwai UA. Detecting different tasks using EEG-source-temporal features. Lecture Notes in Computer science 2012; 7666: 380-387. Schmidt RO. A new approach to geometry of range difference location. IEEE Transactions on Aerospace and Electronic Systems 1972; 821-835. Fasel B, Luettin J .Automatic facial expression analysis: a survey. Pattern Recognition 2003; 36: 259-275. Knapp, Carter (bib0105) 1976; 24 Rabinovich MI, Friston KJ, Varona P. Principles of brain dynamics: global state interactions. MIT Press , 2012. Picard RW,Vyzas E, HealeyJ. Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001; 23:1175-1191. Lisetti C, Nasoz F, leRouge C, Ozyer O, AlvarezK. Developing multimodal intelligent affective interfaces for tele-home health care. International journal of Human-Computer Studies 2003; 59: 245-255. Davidson RJ, Schwartz GE, Saron C, Bennett J,Goleman DJ. Frontal versus parietal EEG asymmetry during positive and negative affect. Psychophysiology 1979; 16: 202-203. 10.1016/j.sbspro.2013.10.204_bib0040 10.1016/j.sbspro.2013.10.204_bib0095 10.1016/j.sbspro.2013.10.204_bib0030 Picard (10.1016/j.sbspro.2013.10.204_bib0035) 2003; 59 10.1016/j.sbspro.2013.10.204_bib0085 10.1016/j.sbspro.2013.10.204_bib0020 10.1016/j.sbspro.2013.10.204_bib0075 10.1016/j.sbspro.2013.10.204_bib0010 10.1016/j.sbspro.2013.10.204_bib0065 10.1016/j.sbspro.2013.10.204_bib0120 10.1016/j.sbspro.2013.10.204_bib0055 10.1016/j.sbspro.2013.10.204_bib0110 10.1016/j.sbspro.2013.10.204_bib0045 10.1016/j.sbspro.2013.10.204_bib0100 Knapp (10.1016/j.sbspro.2013.10.204_bib0105) 1976; 24 10.1016/j.sbspro.2013.10.204_bib0025 10.1016/j.sbspro.2013.10.204_bib0090 10.1016/j.sbspro.2013.10.204_bib0080 10.1016/j.sbspro.2013.10.204_bib0070 10.1016/j.sbspro.2013.10.204_bib0060 10.1016/j.sbspro.2013.10.204_bib0050 10.1016/j.sbspro.2013.10.204_bib0125 10.1016/j.sbspro.2013.10.204_bib0005 10.1016/j.sbspro.2013.10.204_bib0115 |
| References_xml | – reference: Rabinovich MI, Friston KJ, Varona P. Principles of brain dynamics: global state interactions. MIT Press , 2012. – reference: Schuller B, Reiter S, Muller R, Al-Hames M, Lang M, Rigoll G. Speaker independent speech emotion recognition by ensemble classification. IEEE International Conference on Multimedia and Expo, ICME 2005:864-867. – reference: Yeasin M, Bullot B,Sharma R. Recognition of facial expressions and measurement of levels of interest from video. IEEE Transactions on Multimedia 2006; 8: 500-508. – reference: EIAyadi M, Kamel MS, KarrayF. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 2011; 44: 572-587. – reference: Güntekin B, Basar E. Emotional face expressions are differentiated with brain oscillations. International Journal of Psychophysiology2007; 64: 91-100. – reference: Fasel B, Luettin J .Automatic facial expression analysis: a survey. Pattern Recognition 2003; 36: 259-275. – reference: Russell JA. A circumplex model of affect. Journal of personality and social psychology, 1980; 39: 1161-1178. – reference: Aronszajn N. Theory of reproducing kernels. Trans. Amer. Math. Soc1950; 68: 337-404. – volume: 59 start-page: 55 year: 2003 end-page: 64 ident: bib0035 article-title: Affective computing: challenges publication-title: International Journal of Human-Computer Studies – reference: Chanel G, Ansari-Asl K, Pun T. Valence-arousal evaluation using physiological signals in an emotion recall paradigm. IEEE International Conference on Systems, Man and Cybernetics, ISIC 2007: 2662-2667. – reference: Shams WK., Wahab W, Qidwai UA. Detecting different tasks using EEG-source-temporal features. Lecture Notes in Computer science 2012; 7666: 380-387. – reference: Davidson RJ, Schwartz GE, Saron C, Bennett J,Goleman DJ. Frontal versus parietal EEG asymmetry during positive and negative affect. Psychophysiology 1979; 16: 202-203. – reference: Langner O,Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, Knippenberg AV. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 2010; 24: 1377-1388. – volume: 24 start-page: 320 year: 1976 end-page: 327 ident: bib0105 article-title: The generalized correlation method for estimation of time delay. publication-title: IEEE Transactions on Acoustics, Speech and Signal Processing – reference: Lisetti C, Nasoz F, leRouge C, Ozyer O, AlvarezK. Developing multimodal intelligent affective interfaces for tele-home health care. International journal of Human-Computer Studies 2003; 59: 245-255. – reference: Cohen I, Garg, Huang TS. Emotion recognition from facial expressions using multilevel HMM. Neural information processing systems 2000. Citeseer. – reference: Picard RW,Vyzas E, HealeyJ. Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001; 23:1175-1191. – reference: Rumelhart D,Hinton G, Williams R. Learning Internal Representations by Error Propagation. In Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1986. – reference: Atkinson K.E .An introduction to numerical analysis. John Wiley & Sons 1989. – reference: Tacchetti A, Mallapragada PS,Santoro M,RosascoL. GURLS: a Toolbox for Regularized Least Squares Learning. Computer Science and Artifical Intelligence Laboratory , Technical Report CBCL-306, MIT , 2012. – reference: Balconi M , Lucchiari C. EEG correlates (event-related desynchronization) of emotional face elaboration: a temporal analysis. Neuroscience Letters 2006; 392: 118-123. – reference: Murugappan M, Rizon M , Nagarajan R , YaacobS , Zunaidi I , Hazry D. EEG feature extraction for classifying emotions using FCM and FKM. J Comput. Commun 2007; 1: 21-25. – reference: Schmidt RO. A new approach to geometry of range difference location. IEEE Transactions on Aerospace and Electronic Systems 1972; 821-835. – reference: Kayser J, Tenke CE. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: Evaluation with auditory oddball tasks. Clinical Neurophysiology 2006; 117: 348-368. – ident: 10.1016/j.sbspro.2013.10.204_bib0005 – ident: 10.1016/j.sbspro.2013.10.204_bib0125 doi: 10.1016/j.patcog.2010.09.020 – ident: 10.1016/j.sbspro.2013.10.204_bib0120 – volume: 59 start-page: 55 year: 2003 ident: 10.1016/j.sbspro.2013.10.204_bib0035 article-title: Affective computing: challenges publication-title: International Journal of Human-Computer Studies doi: 10.1016/S1071-5819(03)00052-1 – ident: 10.1016/j.sbspro.2013.10.204_bib0080 doi: 10.1080/02699930903485076 – ident: 10.1016/j.sbspro.2013.10.204_bib0100 – ident: 10.1016/j.sbspro.2013.10.204_bib0040 – ident: 10.1016/j.sbspro.2013.10.204_bib0030 doi: 10.1016/S1071-5819(03)00051-X – ident: 10.1016/j.sbspro.2013.10.204_bib0110 doi: 10.21236/ADA164453 – ident: 10.1016/j.sbspro.2013.10.204_bib0045 doi: 10.7551/mitpress/9108.001.0001 – ident: 10.1016/j.sbspro.2013.10.204_bib0065 doi: 10.1109/TAES.1972.309614 – ident: 10.1016/j.sbspro.2013.10.204_bib0070 doi: 10.1016/S0031-3203(02)00052-3 – ident: 10.1016/j.sbspro.2013.10.204_bib0050 doi: 10.1016/j.neulet.2005.09.004 – volume: 24 start-page: 320 year: 1976 ident: 10.1016/j.sbspro.2013.10.204_bib0105 article-title: The generalized correlation method for estimation of time delay. publication-title: IEEE Transactions on Acoustics, Speech and Signal Processing doi: 10.1109/TASSP.1976.1162830 – ident: 10.1016/j.sbspro.2013.10.204_bib0095 doi: 10.1016/j.clinph.2005.08.034 – ident: 10.1016/j.sbspro.2013.10.204_bib0025 doi: 10.1109/34.954607 – ident: 10.1016/j.sbspro.2013.10.204_bib0085 doi: 10.1037/h0077714 – ident: 10.1016/j.sbspro.2013.10.204_bib0075 doi: 10.1109/ICSMC.2007.4413638 – ident: 10.1016/j.sbspro.2013.10.204_bib0010 doi: 10.1109/TMM.2006.870737 – ident: 10.1016/j.sbspro.2013.10.204_bib0020 doi: 10.1109/ICME.2005.1521560 – ident: 10.1016/j.sbspro.2013.10.204_bib0060 doi: 10.1016/j.ijpsycho.2006.07.003 – ident: 10.1016/j.sbspro.2013.10.204_bib0090 doi: 10.1007/978-3-642-34478-7_47 – ident: 10.1016/j.sbspro.2013.10.204_bib0115 doi: 10.1090/S0002-9947-1950-0051437-7 – ident: 10.1016/j.sbspro.2013.10.204_bib0055 |
| SSID | ssj0000313922 |
| Score | 1.8978717 |
| Snippet | This paper proposes a new Electroencephalographic (EEG) emotion recognition system (EEG-ER) that captures human emotion dynamics. EEG signals are collected... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 54 |
| SubjectTerms | 2 Dimension affective model EEG emotion RafD faces relative source temporal features |
| Title | Affective Computing Model Using Source-temporal Domain |
| URI | https://dx.doi.org/10.1016/j.sbspro.2013.10.204 |
| Volume | 97 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA9jJy_iJ84vcvCgh7iPJE1ynNMxFAWZg91K06Qwmd1w8__3vTQdiqDgoYWGPGh_TX95SX_vPUIuEpXBIkNbVhgpmTDwLqzTkmmTGC9ya7zDrYHHp2Q0EfdTOW2QQR0Lg7LKyP0Vpwe2ji3tiGZ7OZu1x12tFHr8gYXBzwce5kKHIL7pzWafBXMTmvAzAfszNKgj6ILMa2VXQFWo8eLXqIuOFdt-zFBfZp3hDtmO7iLtV3e0Sxq-3COtKqaWxu9yRS9j8uirfZL0g0ADOIxWBRtgaqJY8GxOgzqAjsNuPYspqeb0dvGWzcoDMhnevQxGLNZGYDksktZMOOFyLuGRsi4uwbhQmU14N1NOFFLaAisoCd4xruOVBYAcV14VxuLhe5Ifkma5KP0RodAj04XjxhktetboHFykItNeet-xPGkRXuOR5jFxONavmKe1Quw1rVBMEUVsBRRbhG2sllXijD_6qxrq9NsASIHbf7U8_rflCdnCqxBamJyS5vr9w5-Bj7G252EQwfnhWX8C013PPw |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LSwMxEA6iB72IT6zPHBT0EPtIdrM5ePBVWqte2kJv62aThUpti62Iv8s_6Ew2K4qgIPSwl2wSwiR8M5N8M0PIYSgTcDIizTIVBEwo2AttooBFKlRWpFpZg1cDd_dhoytuekFvjrwXsTBIq_TYn2O6Q2vfUvbSLI_7_XK7GkmJFr9DYbDzPbOyZd9ewW-bnDWvYJOParX6deeywXxpAZaCjzFlwgiT8gBmSKrowXAhEx3yaiKNyIJAZ1iASPCKMhUrNcxvuLQyUxo_W8NSEYD7C2B9SESDZu_i82IHkyEq93qBC2S4wiJkz_HKJnoC2IikMn6KRGxfIu6HSvyi5uorZNnbp_Q8F8EqmbPDNVLKg3ipB4IJPfbZqk_WSXjuGCEAmjSvEAG6kGKFtQF1dATads8DzOfAGtCr0VPSH26Q7kwktknmh6Oh3SIUeiRRZrgyKhI1raIUbLIsiWxgbUXzsER4IY849ZnKsWDGIC4oaY9xLsUYpYitIMUSYZ-jxnmmjj_6y0LU8bcTF4My-XXk9r9HHpDFRufuNr5t3rd2yBL-cXGN4S6Znz6_2D0wcKZ63x0oSh5mfYI_AAh7Ckk |
| 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=Affective+Computing+Model+Using+Source-temporal+Domain&rft.jtitle=Procedia%2C+social+and+behavioral+sciences&rft.au=Shams%2C+Wafaa+Khazaal&rft.au=Wahab%2C+Abdul&rft.au=Fakhri%2C+Imad&rft.date=2013-11-06&rft.pub=Elsevier+Ltd&rft.issn=1877-0428&rft.eissn=1877-0428&rft.volume=97&rft.spage=54&rft.epage=62&rft_id=info:doi/10.1016%2Fj.sbspro.2013.10.204&rft.externalDocID=S1877042813036495 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0428&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0428&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0428&client=summon |