{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:31:19Z","timestamp":1743053479954,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031430749"},{"type":"electronic","value":"9783031430756"}],"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\/978-3-031-43075-6_32","type":"book-chapter","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T07:02:17Z","timestamp":1694502137000},"page":"369-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Social and\u00a0Non-social Reward Learning Contexts for\u00a0Detection of\u00a0Major Depressive Disorder Using EEG: A Machine Learning Approach"],"prefix":"10.1007","author":[{"given":"Philopateer","family":"Ghattas","sequence":"first","affiliation":[]},{"given":"Mai","family":"Gamal","sequence":"additional","affiliation":[]},{"given":"Seif","family":"Eldawlatly","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"issue":"15","key":"32_CR1","doi-asserted-by":"publisher","first-page":"4040","DOI":"10.1109\/TSP.2018.2844203","volume":"66","author":"P Ablin","year":"2018","unstructured":"Ablin, P., Cardoso, J.F., Gramfort, A.: Faster independent component analysis by preconditioning with hessian approximations. IEEE Trans. Signal Process. 66(15), 4040\u20134049 (2018)","journal-title":"IEEE Trans. Signal Process."},{"issue":"11","key":"32_CR2","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1109\/TBME.2006.883696","volume":"53","author":"M Aboy","year":"2006","unstructured":"Aboy, M., Hornero, R., Ab\u00e1solo, D., \u00c1lvarez, D.: Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53(11), 2282\u20132288 (2006)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1\u20132","key":"32_CR3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1159\/000438457","volume":"74","author":"UR Acharya","year":"2015","unstructured":"Acharya, U.R., et al.: A novel depression diagnosis index using nonlinear features in EEG signals. Eur. Neurol. 74(1\u20132), 79\u201383 (2015)","journal-title":"Eur. Neurol."},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","volume":"105","author":"FS de Aguiar Neto","year":"2019","unstructured":"de Aguiar Neto, F.S., Rosa, J.L.G.: Depression biomarkers using non-invasive EEG: a review. Neurosci. Biobehav. Rev. 105, 83\u201393 (2019)","journal-title":"Neurosci. Biobehav. Rev."},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Akar, S.A., Kara, S., Agambayev, S., Bilgi\u00e7, V.: Nonlinear analysis of EEG in major depression with fractal dimensions. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7410\u20137413. IEEE (2015)","DOI":"10.1109\/EMBC.2015.7320104"},{"issue":"6","key":"32_CR6","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s10916-005-9001-0","volume":"30","author":"A Alkan","year":"2006","unstructured":"Alkan, A., Kiymik, M.K.: Comparison of AR and Welch methods in epileptic seizure detection. J. Med. Syst. 30(6), 413\u2013419 (2006)","journal-title":"J. Med. Syst."},{"key":"32_CR7","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cmpb.2017.11.023","volume":"155","author":"M Bachmann","year":"2018","unstructured":"Bachmann, M., et al.: Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Comput. Methods Programs Biomed. 155, 11\u201317 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"32_CR8","unstructured":"Bains, N., Abdijadid, S.: Major Depressive Disorder. StatPearls Publishing (2022)"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Bashir, N., Narejo, S., Ismail, F., Anjum, M.R., Prasad, R.: A machine learning framework for major depressive disorder (MDD) detection using non-invasive EEG signals (2022)","DOI":"10.21203\/rs.3.rs-1850559\/v1"},{"issue":"5","key":"32_CR10","first-page":"466","volume":"7","author":"X Bornas","year":"2017","unstructured":"Bornas, X., et al.: Complexity and irregularity in the brain oscillations of depressive patients: a systematic review. Neuropsychiatry 7(5), 466\u2013477 (2017)","journal-title":"Neuropsychiatry"},{"key":"32_CR11","doi-asserted-by":"publisher","first-page":"16756","DOI":"10.1109\/ACCESS.2022.3146711","volume":"10","author":"A Dev","year":"2022","unstructured":"Dev, A., et al.: Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review. IEEE Access 10, 16756\u201316781 (2022)","journal-title":"IEEE Access"},{"issue":"3","key":"32_CR12","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1017\/S0033291719003222","volume":"51","author":"AL Frey","year":"2021","unstructured":"Frey, A.L., Frank, M.J., McCabe, C.: Social reinforcement learning as a predictor of real-life experiences in individuals with high and low depressive symptomatology. Psychol. Med. 51(3), 408\u2013415 (2021)","journal-title":"Psychol. Med."},{"key":"32_CR13","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1016\/j.jad.2019.11.049","volume":"263","author":"AL Frey","year":"2020","unstructured":"Frey, A.L., McCabe, C.: Impaired social learning predicts reduced real-life motivation in individuals with depression: a computational fMRI study. J. Affect. Disord. 263, 698\u2013706 (2020)","journal-title":"J. Affect. Disord."},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ijpsycho.2018.02.002","volume":"132","author":"JE Glazer","year":"2018","unstructured":"Glazer, J.E., Kelley, N.J., Pornpattananangkul, N., Mittal, V.A., Nusslock, R.: Beyond the FRN: broadening the time-course of EEG and ERP components implicated in reward processing. Int. J. Psychophysiol. 132, 184\u2013202 (2018)","journal-title":"Int. J. Psychophysiol."},{"key":"32_CR15","doi-asserted-by":"publisher","first-page":"112850","DOI":"10.1109\/ACCESS.2021.3103047","volume":"9","author":"C Greco","year":"2021","unstructured":"Greco, C., Matarazzo, O., Cordasco, G., Vinciarelli, A., Callejas, Z., Esposito, A.: Discriminative power of EEG-based biomarkers in major depressive disorder: a systematic review. IEEE Access 9, 112850\u2013112870 (2021)","journal-title":"IEEE Access"},{"issue":"2","key":"32_CR16","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/0167-2789(88)90081-4","volume":"31","author":"T Higuchi","year":"1988","unstructured":"Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Physica D 31(2), 277\u2013283 (1988)","journal-title":"Physica D"},{"issue":"3","key":"32_CR17","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.cmpb.2012.10.008","volume":"109","author":"B Hosseinifard","year":"2013","unstructured":"Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal. Comput. Methods Programs Biomed. 109(3), 339\u2013345 (2013)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"32_CR18","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/0010-4825(88)90041-8","volume":"18","author":"MJ Katz","year":"1988","unstructured":"Katz, M.J.: Fractals and the analysis of waveforms. Comput. Biol. Med. 18(3), 145\u2013156 (1988)","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"32_CR19","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1176\/appi.ajp.2018.17101124","volume":"175","author":"H Keren","year":"2018","unstructured":"Keren, H., et al.: Reward processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies. Am. J. Psychiatry 175(11), 1111\u20131120 (2018)","journal-title":"Am. J. Psychiatry"},{"key":"32_CR20","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neubiorev.2016.07.002","volume":"69","author":"A Kupferberg","year":"2016","unstructured":"Kupferberg, A., Bicks, L., Hasler, G.: Social functioning in major depressive disorder. Neurosci. Biobehav. Rev. 69, 313\u2013332 (2016)","journal-title":"Neurosci. Biobehav. Rev."},{"issue":"3","key":"32_CR21","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1152\/jn.90989.2008","volume":"101","author":"KA Ludwig","year":"2009","unstructured":"Ludwig, K.A., Miriani, R.M., Langhals, N.B., Joseph, M.D., Anderson, D.J., Kipke, D.R.: Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J. Neurophysiol. 101(3), 1679\u20131689 (2009)","journal-title":"J. Neurophysiol."},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Mackin, D.M., Nelson, B.D., Klein, D.N.: Reward processing and depression: current findings and future directions. Neurosci. Depression 425\u2013433 (2021)","DOI":"10.1016\/B978-0-12-817935-2.00051-9"},{"issue":"1","key":"32_CR23","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1063\/1.166141","volume":"5","author":"CK Peng","year":"1995","unstructured":"Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A.L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos Interdisc. J. Nonlinear Sci. 5(1), 82\u201387 (1995)","journal-title":"Chaos Interdisc. J. Nonlinear Sci."},{"key":"32_CR24","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-981-16-2008-9_15","volume-title":"Applied Information Processing Systems","author":"D Puri","year":"2022","unstructured":"Puri, D., Nalbalwar, S., Nandgaonkar, A., Wagh, A.: EEG-based diagnosis of Alzheimer\u2019s disease using Kolmogorov complexity. In: Iyer, B., Ghosh, D., Balas, V.E. (eds.) Applied Information Processing Systems. AISC, vol. 1354, pp. 157\u2013165. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-2008-9_15"},{"key":"32_CR25","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circulatory Physiol. 278, H2039\u2013H2049 (2000)","journal-title":"Am. J. Physiol.-Heart Circulatory Physiol."},{"issue":"7","key":"32_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007224","volume":"15","author":"L Safra","year":"2019","unstructured":"Safra, L., Chevallier, C., Palminteri, S.: Depressive symptoms are associated with blunted reward learning in social contexts. PLoS Comput. Biol. 15(7), e1007224 (2019)","journal-title":"PLoS Comput. Biol."},{"key":"32_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1007\/978-3-540-30110-3_147","volume-title":"Independent Component Analysis and Blind Signal Separation","author":"V Sanchez-Poblador","year":"2004","unstructured":"Sanchez-Poblador, V., Monte-Moreno, E., Sol\u00e9-Casals, J.: ICA as a preprocessing technique for classification. In: Puntonet, C.G., Prieto, A. (eds.) ICA 2004. LNCS, vol. 3195, pp. 1165\u20131172. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30110-3_147"},{"issue":"1","key":"32_CR28","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1140\/epjst\/e2008-00833-5","volume":"164","author":"S Schinkel","year":"2008","unstructured":"Schinkel, S., Dimigen, O., Marwan, N.: Selection of recurrence threshold for signal detection. Eur. Phys. J. Spec. Top. 164(1), 45\u201353 (2008)","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Shen, J., Zhao, S., Yao, Y., Wang, Y., Feng, L.: A novel depression detection method based on pervasive EEG and EEG splitting criterion. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1879\u20131886. IEEE (2017)","DOI":"10.1109\/BIBM.2017.8217946"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Strawbridge, R., Young, A.H., Cleare, A.J.: Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatric Dis. Treatment (2017)","DOI":"10.2147\/NDT.S114542"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Sun, S., Chen, H., Shao, X., Liu, L., Li, X., Hu, B.: EEG based depression recognition by combining functional brain network and traditional biomarkers. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2074\u20132081. IEEE (2020)","DOI":"10.1109\/BIBM49941.2020.9313270"},{"key":"32_CR32","unstructured":"Tibdewal, M.N., Mahadevappa, M., Ray, A.K., Malokar, M., Dey, H.R.: Power line and ocular artifact denoising from EEG using notch filter and wavelet transform. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1654\u20131659. IEEE (2016)"},{"issue":"11","key":"32_CR33","doi-asserted-by":"publisher","DOI":"10.2196\/19548","volume":"22","author":"M \u010cuki\u0107","year":"2020","unstructured":"\u010cuki\u0107, M., L\u00f3pez, V., Pav\u00f3n, J.: Classification of depression through resting-state electroencephalogram as a novel practice in psychiatry. J. Med. Internet Res. 22(11), e19548 (2020)","journal-title":"J. Med. Internet Res."},{"issue":"1","key":"32_CR34","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1038\/s41398-022-02064-z","volume":"12","author":"D Watts","year":"2022","unstructured":"Watts, D., Pulice, R.F., Reilly, J., Brunoni, A.R., Kapczinski, F., Passos, I.C.: Predicting treatment response using EEG in major depressive disorder: a machine-learning meta-analysis. Transl. Psychiatry 12(1), 332 (2022)","journal-title":"Transl. Psychiatry"},{"key":"32_CR35","unstructured":"WHO: Depression and other common mental disorders: global health estimates. Technical report, World Health Organization (2017)"},{"issue":"12","key":"32_CR36","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3390\/bios11120499","volume":"11","author":"CT Wu","year":"2021","unstructured":"Wu, C.T., et al.: Resting-state EEG signal for major depressive disorder detection: a systematic validation on a large and diverse dataset. Biosensors 11(12), 499 (2021)","journal-title":"Biosensors"},{"key":"32_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101191","volume":"46","author":"L Zhu","year":"2020","unstructured":"Zhu, L., et al.: EEG-based approach for recognizing human social emotion perception. Adv. Eng. Inform. 46, 101191 (2020)","journal-title":"Adv. Eng. Inform."},{"issue":"6","key":"32_CR38","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/j.1600-0447.1983.tb09716.x","volume":"67","author":"AS Zigmond","year":"1983","unstructured":"Zigmond, A.S., Snaith, R.P.: The hospital anxiety and depression scale. Acta Psychiatr. Scand. 67(6), 361\u2013370 (1983)","journal-title":"Acta Psychiatr. Scand."}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43075-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T07:06:08Z","timestamp":1694502368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43075-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031430749","9783031430756"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43075-6_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hoboken, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wi-consortium.org\/conferences\/bi2023\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CyberChair System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"40","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":"40% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}