{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:13:00Z","timestamp":1778346780928,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031346217","type":"print"},{"value":"9783031346224","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34622-4_11","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T20:25:17Z","timestamp":1686428717000},"page":"137-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Emotion Recognition from EEG Using Mutual Information Based Feature Map and CNN"],"prefix":"10.1007","author":[{"given":"Mahfuza Akter","family":"Maria","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2559-2781","authenticated-orcid":false,"given":"A. B. M. Aowlad","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-8519","authenticated-orcid":false,"given":"M. A. H.","family":"Akhand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. EEE Transactions on Affective Computing. 3, 18\u201331 (2012)","journal-title":"EEE Transactions on Affective Computing."},{"issue":"2","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11042-021-11298-w","volume":"81","author":"A Khattak","year":"2021","unstructured":"Khattak, A., Asghar, M.Z., Ali, M., Batool, U.: An efficient deep learning technique for facial emotion recognition. Multimedia Tools and Applications 81(2), 1649\u20131683 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11298-w","journal-title":"Multimedia Tools and Applications"},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Morais, E., Hoory, R., Zhu, W., Gat, I., Damasceno, M., Aronowitz, H.: Speech emotion recognition using self-supervised features. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6922\u20136926 (2022). https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9747870","DOI":"10.1109\/ICASSP43922.2022.9747870"},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s12193-009-0025-5","volume":"3","author":"L Kessous","year":"2009","unstructured":"Kessous, L., Castellano, G., Caridakis, G.: Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis. Journal on Multimodal User Interfaces. 3, 33\u201348 (2009). https:\/\/doi.org\/10.1007\/s12193-009-0025-5","journal-title":"Journal on Multimodal User Interfaces."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"143293","DOI":"10.1109\/ACCESS.2019.2945059","volume":"7","author":"X Liu","year":"2019","unstructured":"Liu, X., et al.: Emotion recognition and dynamic functional connectivity analysis based on EEG. IEEE Access. 7, 143293\u2013143302 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2945059","journal-title":"IEEE Access."},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Chen, M., Han, J., Guo, L., Wang, J., Patras, I.: Identifying valence and arousal levels via connectivity between EEG channels. In: 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, pp. 63\u201369. IEEE (2015). https:\/\/doi.org\/10.1109\/ACII.2015.7344552","DOI":"10.1109\/ACII.2015.7344552"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1109\/TMM.2016.2614880","volume":"19","author":"S-E Moon","year":"2017","unstructured":"Moon, S.-E., Lee, J.-S.: Implicit analysis of perceptual multimedia experience based on physiological response: a review. IEEE Trans. Multimedia 19, 340\u2013353 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Paradiso, S., et al.: Emotions in unmedicated patients with schizophrenia during evaluation with positron emission tomography (2003). https:\/\/doi.org\/10.1176\/appi.ajp.160.10.1775","DOI":"10.1176\/appi.ajp.160.10.1775"},{"key":"11_CR9","doi-asserted-by":"publisher","unstructured":"Koelsch, S., Fritz, T., Cramon, D.Y.V., M\u00fcller, K., Friederici, A.D.: Investigating emotion with music: An fMRI study. Human Brain Mapping 27, 239\u2013250 (2006). https:\/\/doi.org\/10.1002\/hbm.20180","DOI":"10.1002\/hbm.20180"},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/978-3-642-30448-4_42","volume-title":"Artificial Intelligence: Theories and Applications","author":"C Hondrou","year":"2012","unstructured":"Hondrou, C., Caridakis, G.: Affective, natural interaction using EEG: sensors, application and future directions. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds.) Artificial Intelligence: Theories and Applications, pp. 331\u2013338. Springer, Berlin Heidelberg, Berlin, Heidelberg (2012)"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","volume":"10","author":"SM Alarc\u00e3o","year":"2019","unstructured":"Alarc\u00e3o, S.M., Fonseca, M.J.: Emotions recognition using EEG signals: A survey. IEEE Trans. Affect. Comput. 10, 374\u2013393 (2019). https:\/\/doi.org\/10.1109\/TAFFC.2017.2714671","journal-title":"IEEE Trans. Affect. Comput."},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neunet.2020.08.009","volume":"132","author":"S-E Moon","year":"2020","unstructured":"Moon, S.-E., Chen, C.-J., Hsieh, C.-J., Wang, J.-L., Lee, J.-S.: Emotional EEG classification using connectivity features and convolutional neural networks. Neural Netw. 132, 96\u2013107 (2020). https:\/\/doi.org\/10.1016\/j.neunet.2020.08.009","journal-title":"Neural Netw."},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104757","volume":"136","author":"MR Islam","year":"2021","unstructured":"Islam, M.R., et al.: EEG channel correlation based model for emotion recognition. Comput. Biol. Med. 136, 104757 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104757","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"11_CR14","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s13042-016-0601-4","volume":"9","author":"S Liu","year":"2016","unstructured":"Liu, S., et al.: Study on an effective cross-stimulus emotion recognition model using EEGs based on feature selection and support vector machine. Int. J. Mach. Learn. Cybern. 9(5), 721\u2013726 (2016). https:\/\/doi.org\/10.1007\/s13042-016-0601-4","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.dsp.2018.07.003","volume":"81","author":"A Mert","year":"2018","unstructured":"Mert, A., Akan, A.: Emotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transform. Digital Signal Processing. 81, 106\u2013115 (2018)","journal-title":"Digital Signal Processing."},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Moon, S.-E., Jang, S., Lee, J.-S.: Convolutional neural network approach for EEG-based emotion recognition using brain connectivity and its spatial information. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2556\u20132560 (2018). https:\/\/doi.org\/10.1109\/ICASSP.2018.8461315","DOI":"10.1109\/ICASSP.2018.8461315"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"143303","DOI":"10.1109\/ACCESS.2019.2944273","volume":"7","author":"Z Wang","year":"2019","unstructured":"Wang, Z.: Channel selection method for EEG emotion recognition using normalized mutual information. IEEE Access. 7, 143303\u2013143311 (2019)","journal-title":"IEEE Access."},{"key":"11_CR18","doi-asserted-by":"publisher","unstructured":"Chao, H., Dong, L., Liu, Y., Lu, B.: Improved deep feature learning by synchronization measurements for multi-channel EEG emotion recognition. Complexity. 2020 (2020). https:\/\/doi.org\/10.1155\/2020\/6816502","DOI":"10.1155\/2020\/6816502"},{"issue":"4","key":"11_CR19","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s12021-013-9186-1","volume":"11","author":"G Niso","year":"2013","unstructured":"Niso, G., et al.: HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics 11(4), 405\u2013434 (2013). https:\/\/doi.org\/10.1007\/s12021-013-9186-1","journal-title":"Neuroinformatics"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134, 9\u201321 (2004). https:\/\/doi.org\/10.1016\/j.jneumeth.2003.10.009","DOI":"10.1016\/j.jneumeth.2003.10.009"},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Candra, H., et al.: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7250\u20137253 (2015). https:\/\/doi.org\/10.1109\/EMBC.2015.7320065","DOI":"10.1109\/EMBC.2015.7320065"},{"key":"11_CR22","doi-asserted-by":"publisher","unstructured":"Islam, M., Ahmad, M.: Virtual image from EEG to recognize appropriate emotion using convolutional neural network. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1\u20134 (2019). https:\/\/doi.org\/10.1109\/ICASERT.2019.8934760","DOI":"10.1109\/ICASERT.2019.8934760"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal. 27, 379\u2013423 (1948). https:\/\/doi.org\/10.1002\/j.1538-7305.1948.tb01338.x","journal-title":"The Bell System Technical Journal."},{"key":"11_CR24","unstructured":"Akhand, M.A.H.: Deep Learning Fundamentals- A Practical Approach to Understanding Deep Learning Methods. University Grants Commission of Bangladesh (2021)"},{"key":"11_CR25","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2014)"},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neucom.2017.03.027","volume":"244","author":"P Arnau-Gonz\u00e1lez","year":"2017","unstructured":"Arnau-Gonz\u00e1lez, P., Arevalillo-Herr\u00e1ez, M., Ramzan, N.: Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals. Neurocomputing 244, 81\u201389 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.03.027","journal-title":"Neurocomputing"},{"issue":"3","key":"11_CR27","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s13246-020-00895-y","volume":"43","author":"S Farashi","year":"2020","unstructured":"Farashi, S., Khosrowabadi, R.: EEG based emotion recognition using minimum spanning tree. Physical and Engineering Sciences in Medicine 43(3), 985\u2013996 (2020). https:\/\/doi.org\/10.1007\/s13246-020-00895-y","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Luo, Y., et al.: EEG-based emotion classification using deep neural network and sparse autoencoder. Frontiers in Systems Neuroscience 14 (2020). https:\/\/doi.org\/10.3389\/fnsys.2020.00043","DOI":"10.3389\/fnsys.2020.00043"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Jin, L., Kim, E.Y.: Interpretable cross-subject EEG-based emotion recognition using channel-wise features. Sensors 20 (2020)","DOI":"10.3390\/s20236719"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Machine Intelligence and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34622-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T20:26:24Z","timestamp":1686428784000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34622-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346217","9783031346224"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34622-4_11","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Intelligence and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Noakhali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangladesh","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/confmiet.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"272","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"104","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}