Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of emotion regulation. This mechanism consists of three modules: 1) EEG-based emotional state computation; 2) process-based relaxation assessment; and 3) adaptive positive decision feedback. Collectively, these components form a computation-assessment-feedback closed-loop system that objectively quantifies emotions while enabling real-time decision adjustments based on emotional trends, thereby enhancing user engagement and emotion regulation efficacy through personalized feedback. The contribution of the proposed feedback mechanism was evaluated through a randomized controlled trial (N= 36). The results indicated that both physiological measures and self-reported relaxation significantly increased when compared to interventions without feedback. These findings validate that the EEG-based positive feedback mechanism effectively enhances emotion regulation while providing additional insights into improving both the engagement and effectiveness within digital mental health interventions.

To address channel redundancy and high computational complexity in high-density electroencephalography (EEG) in depression (DP) analysis, this study proposes an elastic net-based double sparse dictionary channel selection (EN-DSDCS) method, to identify core EEG channels and analyze abnormal topological changes in the brain functional network (BFN) of DP patients. An improved coarse-graining method is introduced to reconstruct EEG signals, calculate their multi-scale permutation entropy (MSPE), and construct an MSPE matrix that characterizes signal complexity. Based on this, a double sparse dictionary structure is designed, combining a fixed-base dictionary constructed using the Kronecker product of two DCT(discrete cosine transform) matrices, along with a learning dictionary optimized through iterative sparse K-SVD. The final sparse dictionary D is obtained by multiplying these two components. Subsequently, elastic net regularization jointly optimizes D and the sparse coefficient matrix X, enabling the selection of key channels based on their sparsity levels. The BFN is then constructed using Phase Lag Index (PLI) derived from the selected channels, in order to analyze abnormal changes in network topology and the distribution of Hub node in DP patients. Experimental results demonstrate that EN-DSDCS reduces signal reconstruction error by$\mathbf {3\times 10^{-4}}$, decreases channel sparsity by 3.93% compared with Lasso-based double sparse dictionary channel selection (L-DSDCS), and that most selected channels are located in the frontal and temporal lobes. Moreover, BFN analysis further reveals differential connectivity patterns in these regions among DP patients, with Hub node distribution exhibiting a left hemispheric bias.

With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues.

Steady-state visual evoked potentials (SSVEPs) have been widely applied in brain–computer interfaces, cognitive and clinical neuroscience study. Recently, 40-Hz flickering induced brainwave entrainment (BWE) was used to intervene cognition and aging-related diseases, such as Alzheimer’s disease (AD), vascular cognitive impairment, and phobic anxiety, through 1-h-long visual stimulations. The mechanism of action was believed to induce gamma oscillation in related brain regions. According to our knowledge, the amplitude of BWE at 40 Hz through a prolonged visual stimulation has not been reported. Therefore, we recorded electroencephalogram (EEG) signal induced by a high refreshing rate monitor in 50 healthy subjects. We observed that the response of BWE varied among subjects and fluctuated over time. The relationship between 40-Hz BWE and mental state was analyzed by the brainwave band ratio and signal entropies. We found that the amplitude of BWE was inverse proportionally to brainwave bandα/βratio and correlated with the sample entropy of the recorded EEG, which further connected the BWE response to the fatigue level of the subjects.We also demonstrated that a 1-min stimulation test before the 1-h-long visual stimulation was able to predict the amplitude of BWE. Finally, we showed that the measuredα/βandθ/βratios of EEG were not changed significantly before and after the 1-h-long stimulation. Our experimental results provided database for prolonged BWE, which is beneficial for the design of BWE treatment.

This edition of the publication includes a robust collection of 104 regular papers and features a Special Issue on Knowledge- Infused Learning for Computational Social Systems. This special issue delves into the sophisticated integration of advanced technologies and knowledge-based methodologies within the analysis of computational social systems. Spanning 12 articles, the issue addresses a wide spectrum of topics, from big data management to refining machine learning models with domain-specific insights. It encompasses areas such as energy management in sensor networks, acoustic analysis of heartbeats, detection of fraudulent activities in online ratings, and the management of rumors on social networks, exemplifying the significant role that knowledge-infused learning plays in enhancing technological applications and fostering innovation in social computational systems.

Welcome to the fourth issue of IEEE Transactions on Computational Social Systems (TCSS) in 2023. We are pleased to share some significant developments in our journal. In late June, Clarivate released the updated Impact Factor for all journals indexed by the esteemed Web of Science database. We are delighted to announce that according to the Journal Citation Reports, the 2022 Journal Impact Factor of IEEE TCSS has been determined as 5. Furthermore, we are proud to inform you that IEEE TCSS has achieved a remarkable position in the JCR Category of computer science, cybernetics, ranking in Q1. Thank you once again for your tremendous efforts and unwavering support, which have contributed to the continued success of IEEE TCSS.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) in 2022. In this issue, we are going to present 25 regular articles. After the “scanning the issue,” I would like to share some of my opinions and perspectives on the fundamentals of computational psychophysiology: theory and methodology.

Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.

The rapid development of the COVID-19 pandemic has threatened the lives of people around the world. Many people were caught in anxiety and panic, which also prevents people from fully concentrating on their normal lives. However, the current common neurofeedback therapies used to solve the problem of lack of attention cannot fully deal with the differences in each individual. In addition, direct contact between the patient and the doctor also increases the risk of virus transmission during treatment. This article combines neurofeedback and IoT to establish an adaptive attention adjustment method. IoT connects patients and doctors remotely, reducing the direct contact between them. In order to adapt to individual differences, the feedback indicators of each individual are individually calibrated. In addition, the proportional, integral, and derivative controller was used to adjust the difficulty of the feedback task to adapt to each individual’s self-regulation ability and provide the individual with a higher level of regulation. We also designed adaptive attention adjustment experiments for different individuals. The results show that through adaptive feedback training, the individual’s feedback indicator has dropped by 77.90%, and the individual can adjust his attention state to the individual’s optimal baseline threshold, and the oscillation error gradually reduces to the expected threshold range. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. In the future, this study may provide a general adjuvant treatment for other mental illnesses.

In this paper, we propose a personalized exercise intervention framework based on heart rate variability (HRV) to improve emotion regulation. Firstly, we deeply study the mechanism of exercise improving emotion regulation and introduce an “Emotion Regulation-ANS-Exercise” framework. From this connection, we use heart rate variability as the parameter of biofeedback. Secondly, we quantify a kind of movement state (represented by HRV) which is more beneficial to emotion regulation for each subject through the emotion regulation experiment, and then we design an exercise feedback system by combining biofeedback and PID controller. In this process, we calculate the target speed by the deviation between the target HRV and the current HRV. According to the target speed and real-time speed, the subject will adjust the exercise intensity to make their current HRV close to the target HRV. Finally, through the long-term comparison with the control group, we conclude that the personalized exercise intervention framework designed in this paper is more conducive to emotion regulation.

This study aims to establish an adaptive attention regulation method by combining neurofeedback and classical feedback control theory. It combined open-loop control and closed-loop negative feedback to achieve a universal attention regulation model. The EEG data under attention stimulation was collected., and the baseline threshold range was selected using the optimal threshold selection method., and each individual was calibrated individually. The sliding window method is used to evaluate the individual's attention state and make the feedback training scene to follow its changes. The experiment found that through adaptive feedback training, the individual can adjust the attention state to the individual's optimal baseline threshold. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. Similarly, this study may provide a universal treatment method for other mental diseases.

Depression, threatening the well-being of millions, has become one of the major diseases in the past decade. However, the current method of diagnosing depression is questionnaire-based interviews, which is labor-intensive and highly dependent on doctors' experience. Thus, objective and cost-efficient methods are needed. In this paper, we present a case-based reasoning model for identifying depression. Electroencephalography data were collected using a portable three-electrode EEG device, and then processed to remove artifacts and extract features. We applied multiple classifiers. The best performing k-Nearest Neighbor (KNN) was selected as the evaluation function to select the effective features which were then used to create the case base. Based on the weight set of standard deviations, the similarity was calculated using normalized Euclidean distance to get the optimal recognition rate of depression. The accuracy of optimal similarity identification of patients with depression was 91.25 percent, which was improved compared to the accuracy using KNN classifier (81.44 percent) or previously reported classifiers. Thus, we provide a novel pervasive and effective method for automatic detection of depression.

In many people's daily life, alarm sounds play an important role, which reflects the fast paced life in the modern society. On most occasions, people uses alarm sounds to wake them up in the morning. Improper alarm sounds could make people feel terrible. In this paper, we mainly propose a smart alarm sound recommendation system and construct an application to study how alarm sounds can impact human emotions. The recommendation system is deployed on the cloud, working with smartphones to deliver smart alarm sounds by considering not only sleep patterns, but also context information such as weather. The designed system can recommend smart alarm sounds to users, orchestrate sensing data collected by multiple sensors on smartphones, and collaborate with cloud computing to recommend preferable alarm sounds. An application is developed to demonstrate system effectiveness, which consists of the fore-end on Android OS and the back-end on the cloud. Experiments demonstrate that our system can recommend smart alarm sounds to wake participants up in the morning and the participants give feedback about their emotional states. The results show the system can improve people's emotion states by about 14.57%, compared to traditional alarm sound delivery.

Identifying and evaluating functionally connected regions in the brain has become a challenging problem to solve in many studies of neurological and psychiatric disorders. In particular, functional connectivity of brain mechanisms underlying disturbed cognition in depression is still not well understood in current neuroscience research. Based on the Stroop paradigm, specifically, the face-word Stroop task, we aimed to analyze task-based electroencephalography (EEG) functional connectivity in subjects with depression and in healthy controls, using concepts from time series clustering. In this study, EEG signals of 10 healthy subjects and 10 depressive patients were collected. Then EEG signals were segmented into temporal window corresponding to the event-related potentials (ERPs). For each duration, hierarchical clustering (HC) along with optimizations for the dynamic time warping (DTW) were employed to identify meaningful functionally connected regions and examine changes in depression. It was demonstrated that our method had the potential to become a useful tool for clinical investigators to identify the underlying impairments of brain functional connections in the patients with depression. One of the most representative functional connections obtained in the present study indicated that during the N450 component, the left and right frontal brain parts may discriminate depressive patients from healthy controls. This finding was interpreted by valence-hypothesis, which can prove the validity of the theory of emotional conflict in major depression.

Depression is a mental disorder of high prevalence, leading to a negative effect on individuals, their families, society and the economy. In recent years, the problem of automatic detection of depression from the speech signal has gained more interest. In this paper, a new multiple classifier system for depression recognition was developed and tested. The novel aspect of this methodology is the combination of different speech types and emotions. First of all, using a sample of 74 subjects (37 depressed patients and 37 healthy controls), we examined the discriminative power of different speech types (interview, picture description, and reading) and speech emotions (positive, neutral, and negative). Some voice features (e.g. short time energy, intensity, loudness, zero-crossing rate (ZCR), F0, jitter, shimmer, formants, mel frequency cepstral coefficients (MFCC), linear prediction coefficient (LPC), line spectrum pair (LSP), and perceptual linear predictive coefficients (PLP)) were tested. Then, a new multiple classifier method was proposed to detect depression. It was observed that the overall recognition rate using interview speech was higher than employing picture description speech and reading speech. Furthermore, neutral speech showed better performance than positive and negative speech. Among these features, short time energy, ZCR, LPC, MFCC and LSP were the robust features that gave high accuracy in different types of speech. Finally, this new approach showed a high accuracy of 78.02%, giving high encouragement for detecting depression in speech.

According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations of scalp electrodes, since these locations are closely related to emotions, and uncovered by hair. Special designed experiment has been conducted and totally 178 subjects' EEG data have been collected. Then the research uses KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) to analyze the data. The results show DBN performed better than traditional methods using shallow algorithms. Moreover, the results suggested the absolute power of beta wave is a valid characteristic, which could be used for detection of depression. The accuracy reached 78.24% using the combination of DBN and the absolute power of beta wave. This research proves the feasibility of smaller-size pervasive system for depression diagnosis.

High-dimensional biomedical datasets contain thousands of features used in molecular disease diagnosis, however many irrelevant or weak correlation features influence the predictive accuracy. Feature selection algorithms enable classification techniques to accurately identify patterns in the features and find a feature subset from an original set of features without reducing the predictive classification accuracy while reducing the computational overhead in data mining. In this paper we present an improved shuffled frog leaping algorithm (ISFLA) which explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. Evaluation employs the K-nearest neighbour approach and a comparative analysis with a genetic algorithm, particle swarm optimization and the shuffled frog leaping algorithm shows that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

Emotion is an important indicator of depressive conditions. Emotion recognition based on physiological signals such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) has gained significant attraction in healthcare domain research. Sharing of physiological signal data related to emotional response between different healthcare systems has the potential to benefit both laboratory-based healthcare research and `real-world' clinical practice. However, management and distribution of the data presents significant challenges; addressing these challenges requires advanced tools for data representation, mining and integration. In this paper we propose such a tool which contains an ontology model called EmotionO+ and rules set based on EEG, which is obtained by random forest algorithm to predict emotional state. It presents not only an effective method to enable semantic representation of the EEG and fNIRS data, but also an emotion knowledge mining tool. Results using EEG data in the eNTERFACE'06 dataset show an accuracy for our proposed model of 99.11% as compared to 97.8% for competing methods using the C4.5 algorithm. The experimental results demonstrate that the posited approach is potentially usable for early stage prediction and intervention for depressive disorders.