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Here, we used a multimodal data set of human affective states, which includes electroencephalogram (EEG) and peripheral physiological signals of participants' moment\u2010by\u2010moment reactions to emotional provoking video clips and modeled HR and GSR changes using machine learning techniques, specifically, long short\u2010term memory (LSTM), decision tree (DT), and linear regression (LR). We found that LSTM achieved a significantly lower error rate compared with DT and LR due to its inherent ability to handle sequential data. Importantly, the prediction error was significantly reduced for DT and LR when used together with particle swarm optimization to select relevant\/important features for these algorithms. Unlike summative analysis, and contrary to expectations, we found a significantly lower error rate when the prediction was made across different participants than within a participant. Moreover, the predictive selected features suggest that the patterns predictive of HR and GSR were substantially different across electrode sites and frequency bands. Overall, these results indicate that specific patterns of cerebral activity track autonomic body responses. Although individual cerebral differences are important, they might not be the only factors influencing the moment\u2010by\u2010moment changes in ANS responses.<\/jats:p>","DOI":"10.1111\/psyp.14303","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T11:31:34Z","timestamp":1681385494000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of moment\u2010by\u2010moment heart rate and skin conductance changes in the context of varying emotional arousal"],"prefix":"10.1111","volume":"60","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9689-3290","authenticated-orcid":false,"given":"Harisu Abdullahi","family":"Shehu","sequence":"first","affiliation":[{"name":"School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand"}]},{"given":"Matt","family":"Oxner","sequence":"additional","affiliation":[{"name":"Institute of Psychology University of Leipzig Leipzig Germany"},{"name":"School of Psychology Victoria University of Wellington Wellington New Zealand"}]},{"given":"Will N.","family":"Browne","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Queensland Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0521-2630","authenticated-orcid":false,"given":"Hedwig","family":"Eisenbarth","sequence":"additional","affiliation":[{"name":"School of Psychology Victoria University of Wellington Wellington New Zealand"}]}],"member":"311","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.4324\/9780203880340"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.04.032"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201077117\u20108"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq112"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/CHI.Ob013e318172ef4b"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/emo0000579"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1108\/14668201111177932"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859\u2010019\u20102778\u20105"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2021.3137857"},{"key":"e_1_2_12_11_1","volume-title":"Evolutionary algorithms for solving multi\u2010objective problems","author":"Coello C. 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