No abstract

No abstract

Introduction: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-infrared brain imaging technique.Methods: For demonstration, the decision-making process was constructed in the experiment environment that combined gaming simulator, such as the Iowa Gaming Task (IGT), with functional near-infrared 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-validation 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.Results: During the experiment, the highest correlation was identified in the fourth block between the oxy-hemoglobin signal and IGT score in average value (0.24) and signal feature (0.57). Such relationship is due to block 4 characterization as "conceptual" 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 vectorThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Brain disorders caused by Gaming Addiction drastically increased due to the rise of Internet users and Internet Gaming auditory. Driven by such a tendency, in 2018, World Health Organization (WHO) and the American Medical Association (AMA) addressed this problem as a “gaming disorder” and added it to official manuals. Scientific society equipped by statistical analysis methods such as t-test, ANOVA, and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG), has achieved significant success in brain mapping, examining dynamics and patterns in different conditions and stages. Nevertheless, more powerful, self-learning intelligent algorithms are suitable not only to evaluate the correlation between gaming addiction patterns but also to predict behavior and prognosis brain response depending on the addiction severity. The current paper aims to enrich the knowledge base of the correlation between gaming activity, decision-making, and brain activation, using Machine Learning (ML) algorithms and advanced neuroimaging techniques. The proposed gaming behavior patterns prediction platform was built inside the experiment environment composed of a Functional Near-Infrared Spectrometer (fNIRS) and the computer version of Iowa Gambling Task (IGT). Thirty healthy participants were hired to perform 100 cards selection while equipped with fNIRS. Thus, accelerated by IGT gaming decision-making process was synchronized with changes of oxy-hemoglobin (HbO) levels in the human brain, averaged, and investigated in the left and the right brain hemispheres as well as different psychosomatic conditions, conditionally divided by blocks with 20 card trials in each: absolute unknown and uncertainty in the first block, “pre-hunch” and “hunch” in the second and third blocks, and conceptuality and risky in the fourth and fifth blocks. The features space was constructed around the HbO signal, split by training and tested in two proportions 70/30 and 80/20, and drove patterns prediction ML-based platform consisted of five mechanics, such as Multiple Regression, Classification and Regression Trees (CART), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest. The algorithm prediction power was validated by the 5- and 10-fold cross-validation method and compared by Root Mean Squared Error (RMSE) and coefficient of determination (R Squared) metrics. Indicators of “the best” fit model, lowest RMSE, and highest R Squared were determined for each block and both brain hemispheres and used to make a conclusion about prediction accuracy: SVM algorithm with RBF kernel, Random Forest, and ANN demonstrated the best accuracy in most cases. Lastly, “best fit” classifiers were applied to the testing dataset and finalized the experiment. Hence, the distribution of gaming score was predicted by five blocks and both brain hemispheres that reflect the decision-making process patterns during gaming. The investigation showed increasing ML algorithm prediction power from IGT block one to five, reflecting an increasing learning effect as a behavioral pattern. Furthermore, performed inside constructed platform simulation could benefit in diagnosing gaming disorders, their patterns, mechanisms, and abnormalities.