{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:18:11Z","timestamp":1788740291391,"version":"build-2803163510"},"reference-count":32,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>The current study investigates the utilization and performance of machine learning (ML) algorithms in the cognitive task of finding the correlation between numerical parameters of the human brain activation during gaming. We hypothesize that our integrated feature extraction platform is able to distinguish between different psychosomatic conditions in the gaming process as measured by the functional near\u2010infrared brain imaging technique.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>For demonstration, the decision\u2010making process was constructed in the experiment environment that combined gaming simulator, such as the Iowa Gaming Task (IGT), with functional near\u2010infrared spectroscopy (fNIRS) as the neuroimaging technique. Features of fNIRS levels were extracted, averaged, and synchronized by time with the IGT dataset to predict the task score inside ML algorithms, such as multiple regression, classification and regression trees, support vector machine, artificial neural network, and random forest. For findings validation, the experiment data were resampled by training and testing sets. Further, a training dataset was used to train the ML algorithms, and prediction accuracy was estimated by repeated cross\u2010validation methods and compared by R squared and root mean square error (RMSE). The model with the best accuracy was used with the testing dataset and finalized the experiment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>During the experiment, the highest correlation was identified in the fourth block between the oxy\u2010hemoglobin signal and IGT score in average value (0.24) and signal feature (0.57). Such relationship is due to block 4 characterization as \u201cconceptual\u201d period when participants task experience reaches the maximum, and rewards raise accordingly. Simultaneously, ML algorithms, constructed based on training data set, demonstrate acceptable performance, and RMSE as the primary performance metric dynamically increases from block 1 to block 5, from the state of uncertainty and unknown to the certainty and risky. In contrast, R squared decreases during the same transition. In most IGT blocks, the best fitted model was determined as support vector machine with radial bases function kernel, and predictions were made with the highest accuracy (lowest RMSE) than in training models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Obtained findings showed the applicability and capability of ML models as a powerful technique to evaluate the cognitive neuroimaging task result. Moreover, in terms of features it was identified that the hemodynamic response reacts to the acceleration decision\u2010making process and raises more significance than it was observed before.<\/jats:p><\/jats:sec>","DOI":"10.1002\/brb3.2536","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T18:27:17Z","timestamp":1647368837000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Gaming behavior and brain activation using functional near\u2010infrared spectroscopy, Iowa gambling task, and machine learning techniques"],"prefix":"10.1002","volume":"12","author":[{"given":"Denis","family":"Kornev","sequence":"first","affiliation":[{"name":"Information System Engineering and Management Program Harrisburg University of Science and Technology  Harrisburg Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanley","family":"Nwoji","sequence":"additional","affiliation":[{"name":"Information System Engineering and Management Program Harrisburg University of Science and Technology  Harrisburg Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6418-9859","authenticated-orcid":false,"given":"Roozbeh","family":"Sadeghian","sequence":"additional","affiliation":[{"name":"Data Analytics Program Harrisburg University of Science and Technology  Harrisburg Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Esmaili Sardari","sequence":"additional","affiliation":[{"name":"Computer and Information Systems Program Harrisburg University of Science and Technology  Harrisburg Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1559-5249","authenticated-orcid":false,"given":"Hadis","family":"Dashtestani","sequence":"additional","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"He","sequence":"additional","affiliation":[{"name":"Department of Psychology Southwest University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2652-0162","authenticated-orcid":false,"given":"Amir","family":"Gandjbakhche","sequence":"additional","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8320-5591","authenticated-orcid":false,"given":"Siamak","family":"Aram","sequence":"additional","affiliation":[{"name":"Information System Engineering and Management Program Harrisburg University of Science and Technology  Harrisburg Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"American Psychiatric Association(2018).Internet gaming.https:\/\/www.psychiatry.org\/patients\u2010families\/internet\u2010gaming"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003019855-10"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Aram S. 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Gandjbakhche A. &Aram S.(2021).Neural Correlation of brain activities and gaming using functional near\u2010infrared spectroscopy and iowa gambling task.International Conference on Applied Human Factors and Ergonomics (AHFE 2020) Advances in Neuroergonomics and Cognitive Engineering 16\u201322.","DOI":"10.1007\/978-3-030-51041-1_3"},{"issue":"2907","key":"e_1_2_8_18_1","first-page":"1","article-title":"Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion state","volume":"15","author":"Kumar S.","year":"2018","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.11.004"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbr.2018.09.021"},{"issue":"3","key":"e_1_2_8_21_1","first-page":"69","article-title":"Statistics corner: A guide to appropriate use of correlation coefficient in medical research","volume":"24","author":"Mukaka M. 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