Functional near-infrared spectroscopy (fNIRS) has shown potential in attention deficit/hyperactivity disorder (ADHD) research, though it is not yet widely used as a primary diagnostic tool. While most previous studies have focused on children and resting-state conditions, research on adult ADHD, particularly under task-state conditions, is increasing but still limited compared to studies on children. Since ADHD is associated with cognitive challenges and alterations in brain activity, investigating functional connectivity during a task can provide a better understanding of its neural characteristics. In this study, we aim to investigate functional connectivity in adult patients with ADHD by comparing them with healthy controls under task-state conditions. We used the fNIRS dataset, which comprised 75 healthy controls and 75 medication-naïve individuals with ADHD. The network characteristics of functional connectivity were compared during a verbal fluency task, specifically focusing on density, global clustering coefficient, efficiency, and average betweenness centrality. By statistical analysis between the two groups, statistical significance was observed in density (p<0.001, t = 5.39, η2 = 0.443). Additionally, various machine learning classifiers were employed to assess the potential of functional connectivity metrics in classifying the two groups. The linear support vector machine achieved accuracy and precision of 0.800, recall of 0.808, and F1-score of 0.799, representing the highest performance among five different classifiers. In conclusion, our findings reveal distinct functional connectivity patterns among the groups, highlighting the potential of fNIRS-derived functional connectivity metrics as biomarkers for ADHD.

Self-disclosure involves revealing information about oneself to others and is critical in relationship formation to develop trust and understanding, leading to emotional intimacy. In psychotherapy, inducing self-disclosure is critical for a therapist to clearly understand clients and suggest relevant solutions. Companion animals have been known to increase human self-disclosure; hence, we hypothesized that virtual animals could have the same effect, which can be strengthened through interaction. To verify this hypothesis, we implemented a mixed-reality-based interaction between humans and virtual cats through Unity. Participants could interact with the virtual cat using hand gestures and voice commands. Psychological states related to self-disclosure were evaluated using questionnaires after the interaction. Furthermore, participants’ responses to the virtual cats were compared with their responses to non-interactive virtual contents. Participants exhibited higher willingness for self-disclosure with virtual cats compared with virtual humans. Interacting with virtual cats was also found to encourage more self-disclosure than interactions with non-interactive states content. Therefore, virtual animals can induce self-disclosure and can be used in psychotherapy. Our findings demonstrate that virtual animals can be used to provide solutions to mental health problems and can be widely applied in the field of psychotherapy.

From electroencephalography (EEG) data during self-relevant sentence reading, we were able to discriminate two implicit intentions: 1) "agreement" and 2) "disagreement" to the read sentence. To improve the classification accuracy, discriminant features were selected based on Fisher score among EEG frequency bands and electrodes. Especially, the time-frequency representation with Morlet wavelet transforms showed clear differences in gamma, beta, and alpha band powers at frontocentral area, and theta band power at centroparietal area. The best classification accuracy of 75.5% was obtained by a support vector machine classifier with the gamma band features at frontocentral area. This result may enable a new intelligent user-interface which understands users' implicit intention, i.e., unexpressed or hidden intention.

We propose a novel experiment paradigm to measure human trust on machine during a collaborative and egoistic theory-of-mind game. To show a different level of human trust on machine partners, we control the technical capability and humanlike cues of the autonomous agent in the cognitive experiments while recording participant's electroencephalography (EEG). The measured human trust values at various situations will be used to develop a dynamic trust model for efficient human-machine systems.

The objective of human computer interface (HCI) research is to make machine that understands human intention with high accuracy. In the future, machine will be able to understand human intention and communicate with human even though there is no explicit expression such as speech, gesture, etc. In this study, a new experiment design is proposed to understand human intention by using electroencephalography (EEG). Two types of stimulus are given to the subjects; affirmative and negative sentences. Each sentence was separated by two blocks which are contents and sentence end. Subjects were asked to make a decision of agreement or disagreement after sentence end is shown. Based on the EEG analysis, intention to the sentences was found while seeing contents, i.e., before giving an explicit answer. It is shown that decision making is done before the end of the sentence, and answering “Yes” or “No” is determined only up to the sentence end. This study shows the relationship of implicit intention and average event-related potentials (ERPs) at the frontal sites and how to predict implicit intention based on the central frontal ERPs using support vector machine (SVM).