The brain patterns of emotional perception remain a pivotal research domain in affective neuroscience. Modeling the brain as a complex network has become a crucial approach to understanding its functions. However, traditional brain network research based on graph theory primarily focuses on dyadic interactions between brain regions, which cannot effectively characterize the information exchange process among multiple brain regions during emotional cognitive processes. To address these limitations, we shift our perspective from graph theory to the topological data analysis (TDA) of minimal 1-cycles. The 1 cycles or loops within a network represent the fundamental high order interactions in complex networks and serve as essential pathways for information transmission and integration among the distributed networks of brain regions. By focusing on cycle structures in affective brain networks, we propose a novel SMA-EL method based on the collaborative optimization of the Minimal 1-Cycle with Simplicial Maps Annotation (SMA-M1C) method and linear programming, which balances computational efficiency and method performance to reconstruct the optimal cycles in the brain network. This method is applied to the analysis of emotional brain networks in response to positive and negative emotions induced by naturalistic viewing. Comprehensive experiments demonstrate that the 1-cycle structures of the brain's functional patterns exhibit differences at both individual and group levels, aligning with prior research. Furthermore, the 1 cycles we proposed can serve as a biological marker for emotion recognition. These findings may provide new insights into the organization patterns of functional brain networks under diverse emotional states.

Researchers have long sought objective and quantifiable methods for recognizing depression. Electroencephalography (EEG) signals, which reflect brain activities objectively, have emerged as a promising tool for this purpose. However, the practical application of EEG signals faces significant challenges arising from distribution variability across different datasets and subjects. In addition, conventional methods often struggle to effectively capture information related to dynamic transformations in distributions. To address these issues, we propose a Wasserstein distribution-inspired dynamic adversarial network (WDANet) for EEG-based depression recognition. Specifically, WDANet includes a global discriminator that focuses on the marginal distribution of EEG features, a local discriminator that concentrates on the conditional distribution of EEG features, and a Wasserstein distribution discriminator that utilizes Wasserstein distributions derived from various processed EEG features. The experimental results show that WDANet achieved classification accuracies of 83.33%, 75.52%, 73.93%, 76.04%, and 70.94% in cross-subject, cross-dataset experiments conducted on three datasets, demonstrating its effectiveness and superiority compared to state-of-the-art methods. These results support our claim that WDANet enhances the accuracy and interpretability of depression recognition, providing insights and new research directions for the integration of neuroscience and artificial intelligence technologies.

With the rapid advancement of emotion recognition technology, multimodal physiological signals have garnered increasing research attention due to their rich affective representations. However, the substantial heterogeneity across different physiological modalities poses a significant challenge for effective multimodal fusion, limiting the performance of current emotion recognition systems. Moreover, while demographic information inherently encodes valuable emotional cues, its systematic integration into emotion recognition remains underexplored. To address these challenges, we propose R2G$^{3}$Net, a novel hierarchical framework for multimodal emotion recognition. Our model leverages a three-tier architecture: 1) Regional-to-Global Brain Feature Extraction: A BiLSTM-GNN hybrid network hierarchically encodes EEG signals, capturing spatio-temporal patterns from local brain regions to global functional connectivity. 2) Regional-to-Global Cross-Modal Fusion: Peripheral nervous system (PNS) signals are extracted and fused with brain features to enhance physiological representation learning. 3) Regional-to-Global Social Context-Aware Modeling: A hypergraph neural network (HGNN) integrates demographic data to construct dynamic social networks, uncovering higher-order emotional interactions for improved interpretability. Extensive experiments on three benchmark datasets demonstrate R2G$^{3}$Net's superiority in joint spatio-temporal feature learning and social context-aware emotion recognition. Ablation studies and visual analytics further validate that our fused representations outperform state-of-the-art methods in both discriminative capability and model transparency.

To objectively evaluate the intervention effectiveness of exercise rehabilitation on drug addicts, this study proposes a neural-assessment method for measuring rehabilitation efficacy. Taking subjects from isolation rehabilitation centers as the research objects, they were divided into an experimental group (receiving exercise rehabilitation training), a control group (not receiving exercise rehabilitation training), and a newly admitted group (just admitted to the rehabilitation center). Brain activities of the three groups under resting state and audio-stimulated state were evaluated, and power features and nonpower features were extracted. A comparative study between groups was conducted by combining statistical analysis and machine learning models. The results show that the power index of the FPz channel is the most sensitive for distinguishing whether exercise intervention is received, and the nonpower features of the FP1 and FP2 channels are the core basis for identifying different withdrawal stages. Various machine learning models have achieved effective identification of subjects in different intervention states and withdrawal stages, confirming the reliability and potential of our method for evaluating exercise rehabilitation effectiveness. This study provides technical support and theoretical basis for optimizing exercise-assisted rehabilitation strategies and improving drug control governance effectiveness.

Depression has become one of the most prevalent mental health disorders worldwide, highlighting the urgent need for objective and reliable auxiliary diagnostic methods. Electroencephalography (EEG), as a non-invasive technique with high temporal resolution, shows great promise in depression recognition. However, the significant inter-subject variability inherent in EEG signals limits the generalization ability of traditional machine learning and deep learning models in cross-subject scenarios. Although incorporating multi-source data can enhance the representational capacity of transfer learning, it also introduces new challenges, such as distributional conflicts and adaptation strategy inconsistencies between sources, which can lead to negative transfer. To address these issues, we propose a Multi-Source Dynamic Adversarial Adaptation Network (MS-DAAN). This framework constructs independent feature extraction and adversarial adaptation branches for each source domain, incorporates an unsupervised EEG-based source clustering mechanism to form semantically coherent subdomains, and introduces a target-guided source attention module to dynamically weight each source according to its statistical similarity to the target domain. Experimental results demonstrate that MS-DAAN significantly outperforms existing methods across multiple evaluation metrics, validating its effectiveness and robustness in cross-subject EEGbased depression recognition.

Early subthreshold depression identification is critical for intervention but hampered by subjective assessments. Gait analysis offers a promising objective biomarker, but existing computational methods struggle to integrate multi-scale motor patterns and lack the interpretability required for clinical trust. This paper introduces$\mathbf{X}^{\mathbf{2}}$-Gait, a novel deep learning framework designed to address these challenges.$\mathrm{X}^{2}$-Gait pioneers a hierarchical, context-aware mechanism that extracts features at multiple granularities, from fine-grained joint kinematics to holistic body posture. Crucially, it designates macro-level postural features as a “contextual anchor” to dynamically guide the interpretation of micro-level movement features via a gating network. This architecture uniquely enables dual-level interpretability, providing both a high-level postural assessment and specific, corroborating biomechanical evidence for each prediction. Evaluated on a dataset of individuals with subthreshold depression and healthy controls,$\mathbf{X}^{\mathbf{2}}$-Gait not only achieves state-of-theart classification accuracy$(70.26 \%)$but also generates clinically meaningful, hierarchical explanations. This work represents a significant step toward developing transparent, trustworthy, and non-intrusive tools for mental health screening.

Photoplethysmography (PPG) signals have been extensively used for monitoring stress level and improving human mental health. A major obstacle to improving PPG classification is the scarcity of real signals, necessitating the employment of signal synthesis techniques. Incorporating the dynamics of PPG into the generative adversarial network (GAN) helps model the physiological dynamics and improves synthesis quality. However, differential equations that can describe the dynamic characteristics of PPG are unavailable. To address this issue, we propose a novel generative adversarial network for PPG synthesis with data-driven attractor constraints (AC-GAN). Firstly, we design a recurrent neural network (RNN), which can continuously update its hidden states, to extract the chaotic motion characteristics of real PPG signals in a purely data-driven mode. Subsequently, the pre-trained attractor extraction network is used as a prior in the optimization process of a GAN to create PPG signals that conform to the underlying dynamics of physiological systems. Several experiments on three public datasets demonstrate that the signals generated by AC-GAN are optimal in terms of both similarity and usability compared to several state-of-theart methods.

Depression is a prevalent mental illness that significantly impacts the well-being of individuals and the development of society. The current diagnostic methods are largely subjective and time-consuming. Moreover, the existing machine learning-based depression recognition methods struggle to fully exploit the collaborative benefits between modalities, lack interpretability in their fusion processes, and perform inadequately in few-shot depression recognition tasks. To address these challenges, we propose a meta-fuzzy multimodal fusion network (MF$^{2}$-Net) for depression recognition. This innovative approach integrates physiological signals and behavioral data, employs multiple MLPs to learn the fuzzy measures of single base learners and complementary increments, then constructs all fuzzy measures, and finally achieves an interpretable decision-level fusion process through fuzzy integrals. Furthermore, we incorporate model-agnostic meta-learning for conducting few-shot domain-adaptive training, mitigating the issues related to high individual variability levels and the scarcity of multimodal depression data. Our method demonstrates exceptional classification performance in subject-independent experiments implemented on public datasets; offers a reliable solution for objectively, effectively, and conveniently recognizing depression; and has the potential to promote the clinical applications of rapid intelligent depression diagnosis.

As an essential challenge within the realm of affective computing, emotion recognition assumes a vital role in bestowing computers with a higher level and comprehensive intelligence. Furthermore, it has emerged as a crucial research topic in both human–computer interaction (HCI) and medical rehabilitation related to mental illnesses. However, the related fusion studies for modeling physiological signals in emotion recognition are less based on multimodal coordinated representation and lack the exploration of multimodal physiological signal correlation. In this article, we propose a tensor correlation fusion framework for emotion recognition based on multimodal physiological signals. After extracting effective features from various physiological signals, the coordinated representation module of the framework first simultaneously learns the linear correlation of all input physiological signals based on the covariance tensor. An optimized solution strategy is constructed to obtain the coordinated representation corresponding to each physiological signal. Finally, an emotion recognition module fuses the correlation information of the coordinated representation of different physiological signals as input to the emotion recognition classifier. This framework constructs coordinated representation by introducing a strategy to simultaneously capture the correlation among multiple physiological signals, providing a fresh perspective with a well-defined mathematical foundation for the fusion of multimodal physiological signals in the realm of emotion recognition. The experiments conducted on the DEAP dataset demonstrate that compared with related methods, the framework achieves relatively higher emotion recognition performance while obtaining a coordinated representation of multimodal physiological signal correlations of emotions, all while achieving superior processing speed.

Intelligence evaluation is a desirable intelligent application for sensing and interaction in various scenarios, e.g., education, office, and the aviation industry. For example, identifying gifted students, who learn faster and more efficiently than general students due to their neurophysiological advantages, and teaching different students according to their intelligence are urgent requirements in school education. However, current intelligence evaluation mainly relies on intelligence quotient (IQ) tests, which have a problem of decreasing reliability in repeated tests. In addition, no objective assessment criteria are available in the present intelligence evaluation process. Electroencephalogram (EEG) signals, which reflect the neuroelectrical activities of the brain, can be utilized to develop an objective and promising tool for investigating the neurophysiological advantages of gifted groups and augmenting the effects of intelligence evaluation. Consequently, we proposed a novel real-time intelligence evaluation framework based on users’ psychophysiological data. Then, we leveraged the framework to investigate a case study to asses which EEG patterns could be used to effectively characterize gifted students and distinguish them from average students. Experimental results reveal the great differences in the chaos degree of the brain (CDB) between different groups of subjects and the effectiveness of the model in identifying gifted students, thus verifying the practicability and validity of the proposed framework.

Modeling correlations between multimodal physiological signals [e.g., canonical correlation analysis (CCA)] for emotion recognition has attracted much attention. However, existing studies rarely consider the neural nature of emotional responses within physiological signals. Furthermore, during fusion space construction, the CCA method maximizes only the correlations between different modalities and neglects the discriminative information of different emotional states. Most importantly, temporal mismatches between different neural activities are often ignored; therefore, the theoretical assumptions that multimodal data should be aligned in time and space before fusion are not fulfilled. To address these issues, we propose a discriminative correlation fusion method coupled with a temporal alignment mechanism for multimodal physiological signals. We first use neural signal analysis techniques to construct neural representations of the central nervous system (CNS) and autonomic nervous system (ANS). respectively. Then, emotion class labels are introduced in CCA to obtain more discriminative fusion representations from multimodal neural responses, and the temporal alignment between the CNS and ANS is jointly optimized with a fusion procedure that applies the Bayesian algorithm. The experimental results demonstrate that our method significantly improves the emotion recognition performance. Additionally, we show that this fusion method can model the underlying mechanisms in human nervous systems during emotional responses, and our results are consistent with prior findings. This study may guide a new approach for exploring human cognitive function based on physiological signals at different time scales and promote the development of computational intelligence and harmonious human-computer interactions.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum-like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum-like block.

Depression is a prevalent mental disorder that poses significant risks to human health and social stability. Current methods for diagnosing depression heavily rely on patient descriptions and psychiatrist observations, which are susceptible to interference from subjective factors and carry the risk of misdiagnosis and missed diagnosis. Therefore, it is crucial to develop an objective method for recognizing depression based on objective criteria. Recently, combining EEG signals with deep learning techniques for depression recognition has become a popular research topic. However, existing EEG-based depression recognition methods are poorly interpretable, making it challenging to explain the neural mechanisms of depression disorders. Consequently, we propose an explainable framework for depression recognition from EEG signals based on a GCN. In this method, a hybrid module of 1DCNN, LSTM and GCN is utilized to extract features from EEG signals, which can effectively capture spatiotemporal correlations between different brain regions. The EEG subgraph construction module explores the differences in crucial connectivity patterns of the brain between different groups, enhancing the interpretability of our model. The experimental results on the MODMA dataset show that our model outperforms the baseline model in all metrics, thus verifying the validity of the proposed model. Additionally, compared with existing explainable algorithms, our method consistently yielded nearly identical experimental results, demonstrating its ability to capture the correlation between depression and neuroscience, and has good interpretability.

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Depression is a serious and common psychiatric disease characterized by emotional and cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression are low. Therefore, the accurate recognition of depression is important for its effective treatment. Electroencephalogram (EEG) signals, which can objectively reflect the inner states of human brains, are regarded as promising physiological tools that can enable effective and efficient clinical depression diagnosis and recognition. However, one of the challenges regarding EEG-based depression recognition involves sufficiently optimizing the spatial information derived from the multichannel space of EEG signals. Consequently, we propose an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address this challenge. In this method, we propose two improved FL functions that can enhance the separability of hard examples by upweighting their losses as optimization objectives and can optimize the channel weights by a proposed adaptive channel fusion framework. The experimental results obtained on two EEG datasets show that the developed channel fusion method can achieve improved classification performance. The learned channel weights include the individual characteristics of each EEG epoch, which can effectively optimize the spatial information of each EEG epoch via the channel fusion method. In addition, the proposed method performs better than the state-of-the-art channel fusion methods.

Depression is a mental disorder characterized by persistent low mood that affects a person’s thoughts, behavior, feelings, and sense of well-being. According to the World Health Organization (WHO), depression will become the second major life-threatening illness in 2020. Electroencephalogram (EEG) signals, which reflect the working status of human brain, are regarded as the best physiological tool for depression detection. Previous studies used the Empirical Mode Decomposition (EMD) method, which can deal with the highly complex, nonlinear and non-stationary nature of EEG, to extract features from EEG signals. However, for some special data, the neighboring components extracted through EMD could certainly have sections of data carrying the same frequency at different time durations. Thus, the Intrinsic Mode Functions (IMFs) of the data could be linearly dependent and the features coefficients of expansion based on IMFs could not be extracted, which can make the pre-proposed EMD-based feature extraction method impractical. In order to solve this problem, an improved EMD applying Singular Value Decomposition (SVD)-based feature extraction method was proposed in this study, which can extract the features coefficients of expansion based on all IMFs as accurately as possible, ignoring potentially linear dependence of IMFs. Experiments were conducted on four EEG databases for detecting depression. The improved EMD-based feature extraction method can extract feature from all three channels (Fp1, Fpz, and Fp2) on the four EEG databases. The average classification results of the proposed method on the four EEG databases including depressed patients and healthy subjects reached 83.27, 85.19, 81.98 and 88.07 percent, respectively, which were comparable with the pre-proposed EMD-based feature extraction method.

Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.

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.

These days, physiological signals have been studied more broadly for emotion recognition to realize emotional intelligence in human-computer interaction. However, due to the complexity of emotions and individual differences in physiological responses, how to design reliable and effective models has become an important issue. In this article, we propose a regularized deep fusion framework for emotion recognition based on multimodal physiological signals. After extracting the effective features from different types of physiological signals, we construct ensemble dense embeddings of multimodal features using kernel matrices, and then utilize a deep network architecture to learn task-specific representations for each kind of physiological signal from these ensemble dense embeddings. Finally, a global fusion layer with a regularization term, which can efficiently explore the correlation and diversity among all of the representations in a synchronous optimization process, is designed to fuse generated representations. Experiments on two benchmark datasets show that this framework can improve the performance of subject-independent emotion recognition compared to single-modal classifiers or other fusion methods. Data visualization also demonstrates that the final fusion representation exhibits higher class-separability power for emotion recognition.

Driver fatigue has become a leading cause of accidents and death in the transportation industry. Electroencephalography (EEG)-based fatigue detection can be a good way to reduce accidents and improve safety and efficiencies throughout the transportation system. In this article, we focus on investigating whether the spatial–temporal changes in the relations between EEG channels are specific to different driving states. EEG signals were first partitioned into several segments, and the covariance matrices obtained from each segment were input into a recurrent neural network to extract high-level temporal features. Meanwhile, the covariance matrices of whole signals were leveraged to extract spatial characteristics that were fused with temporal features to obtain comprehensive spatial–temporal information. In experiments on an open benchmark dataset, our method achieved an excellent classification accuracy of 89.28% and showed superior performance compared to several other state-of-the-art methods. These results indicate that our method can enable higher performance in driver fatigue detection.

Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Consequently, we propose an optimal channel selection method for EEG-based depression detection via kernel-target alignment (KTA) to effectively resolve the abovementioned issues. In this method, we consider a modified version KTA that can measure the similarity between the kernel matrix for channel selection and the target matrix as an objective function and optimize the objective function by a proposed optimal channel selection strategy. Experimental results on two EEG datasets show that channel selection can effectively increase the classification performance and that even if we rely only on a small subset of channels, the results are still acceptable. The selected channels are in line with the expected latent cortical activity patterns in depression detection. Moreover, the experimental results demonstrate that our method outperforms the state-of-the-art channel selection approaches.

The World Health organization (WHO) stated that the concept of health has been widened to subjectively experienced dimensions such as fatigue and chronic fatigue syndrome (CFS). With the increasing pressure of the current life, persistent fatigue caused by sustained high-pressure work will not only be hazardous to health, but also give rise to unexpected consequences. In particularly, fatigue driving induced by long time driving has become a leading cause of accidents and death in the transportation. In this study, we investigate electroencephalography(EEG)-based fatigue detection of drivers through the spatial-temporal changes in the relations between EEG channels. EEG signals are firstly partitioned into several segments and the covariance matrices obtained from each segment are fed into a recurrent neural network to extract high-level temporal features. Then, the covariance matrices of whole signals are leveraged to extract spatial characteristics, which will be fused with temporal features to obtain comprehensive spatial-temporal information. Experimental results on a benchmark dataset showed that our method obtained an optimal classification accuracy of 91.042% and outperformed some state-of-the-art methods. These results indicate that our method is reliable and feasible for fatigue detection, which also provides a novel solution for EEG modeling.

Depression is a mental disorder characterized by emotional and cognitive dysfunction, which appears a state of low mood and aversion to activity. Depression can affect a person's thoughts, behavior, feelings, and sense of well-being. Depression is projected to be the second major life-threatening illness in 2020 by World Health Organization (WHO). Thus, it is urgent to detect and treat depression. Electroencephalogram (EEG) signals, which objectively reflect the working status of the human brain, are considered as promising physiological tools for depression detection. Negatively biased processing of affective stimuli in depression has been proven. In order to detect depression more effectively, we proposed an affective auditory stimuli induced depression detection method from EEG signals. In this method, we applied negative, positive and neutral affective auditory stimuli with several frequency selected from the International Affective Digitized Sounds (IADS-2) to induce negative affective bias in patients with depression. We synchronously collected EEG signals with three electrodes located on the prefrontal lobe (Fpl, Fpz, and Fp2), then extracted efficacious features by Empirical Mode Decomposition (EMD) based feature extraction method to detect depression effectively. The results of the proposed method showed that high-frequency affective auditory stimuli were more effective in depression detection and the frequency of affective auditory stimuli was a crucial property, which can influence the effectiveness of affective auditory stimuli in depression detection.

Intelligent recognition of electroencephalogram (EEG) signals has been an important means to recognize emotions. Traditional user-independent method, which treatseach individual's EEG data as independent and identically distributed (i.i.d.) samples and ignores destruction on i.i.d. condition caused by individual differences, usually has lower generalization performance. Although user-dependent method could alleviate abovementioned problem, it faces difficulty in collection of sufficient training EEG data for each individual. In order to construct user-dependent model merely based on a small amount of training EEG data, we incorporate transfer learning framework and propose a individual similarity guided transfer modeling method for EEG-based emotion recognition. We first measure the similarities between individuals using maximum mean discrepancy (MMD), then utilize pre-existing EEG data of similar individuals to assist construction of user-dependent model for the target individual using an instance-based transfer learning algorithm named TrAdaBoost. We compared this method with traditional user-independent and user-dependent methods on DEAP dataset. Experimental results showed that our method could transfer useful knowledge from other individuals for user-dependent emotion recognition, which achieved classification accuracies of 66.1% and 66.7% on arousal and valence dimentions, respectively.

Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. According to the study of World Health Organization (WHO), depression will become the second largest cause of illness threatening the life of human beings in 2020, so early detection, early diagnosis and early treatment of depression is very important to save the health and life of human beings. In order to alleviate the damage caused by depression and make early detection, early diagnosis and early treatment of depression, a portable and accurate depression detection and diagnosis method is most necessary. Due to the highly complexity, nonlinearity and non-stationarity of electroencephalogram (EEG) data in nature, we present a novel method for pervasive EEG-based detection and diagnosis of depression with the resting state eye-closed EEG data of Fp1, Fpz and Fp2 locations of scalp electrodes, which are closely related to emotion, collected through three-electrode pervasive EEG collection device in this paper. Experiment has been conducted and totally 170 (81 depressive patients and 89 normal subjects) subjects' pervasive EEG data have been collected in resting state and eye-closed. Then, Support Vector Machine (SVM) is utilized to analyze the pervasive EEG data and the average accuracy reaches 83.07%. After Friedman Test and post-hoc two-tailed Nemenyi Test, we propose a splitting criterion for pervasive EEG. The data analysis experimental results show that the proposed method for detecting and diagnosing depression is effective and convenient, and it also demonstrate that the three-electrode pervasive EEG collection device has broad prospects in depression detection and diagnosis.

Recently, Electroencephalogram (EEG) has become increasingly important in the role of psychiatric diagnosis and emotion recognition. However, many irrelevant features make it difficult to identify patterns accurately. Obtaining valid features from electroencephalogram can improve the classification and generalization performance. In this paper, an improved normalized mutual information feature selection algorithm which is based on Grassberger entropy estimator (G-NMIFS) is proposed for EEG data. We employ the k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Naïve Bayes methods to compare the proposed approach with normalized mutual information feature selection using Naïve estimator and Miller-adjust method. Experimental results on two EEG data sets show that the proposed method can select relevant subsets and improve classification performance effectively.