Psychological analysis related to voluntary reciprocal trust games were obtained using functional magnetic resonance imaging (fMRI) hyperscanning for 44 pairs of strangers throughout 36 trust games (TG) and 16 control games (CG). Hidden Markov models (HMMs) are proposed to train and classify the fMRI data acquired from these brain regions and extract the essential features of the initial decision of the first player to trust or not trust the second player. These results are evaluated using the different versions of the multifold cross-validation technique and compared to other speech data and other advanced signal processing techniques including linear classification, support vector machines (SVMs), and HMMs. With above 80% classification accuracy for HMM as compared to no more than 66% classification accuracy of a linear classifier and SVM, the corresponding experimental results demonstrate that the HMMs can be adopted as an outstanding paradigm to predict the psychological financial (trust/non-trust) activities reflected by the neural responses recorded using fMRI. Additionally, extracting the specific decision period and clustering the continuous time series proved to increase the classification accuracy by almost 20%
