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.

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.

EEG emotion recognition presents several challenges, including region correlation, local and long-range node connectivity, and multi-channel patterns, necessitating advanced methods capable of effectively capturing and utilising complex EEG signal information. This paper introduces a novel method, the dual-gate Chebyshev Linkage network (DGC-Link), which comprises three main components: the Chebyshev Linkage (CL) module for extracting regional correlation features, the dual-gate module for regulating the flow of different-order information, and the deep network for extracting multi-channel features and enhancing representation capabilities. Validated on three datasets (SEED, DREAMER, and MPED) with ablation experiments demonstrating each component's effectiveness, DGC-Link achieves superior recognition performance compared to state-of-the-art methods. Notably, it achieves 96.43% accuracy on differential entropy on the SEED dataset, and 98.58%, 97.62%, and 98.01% for valence, arousal, and dominance classifications on the DREAMER dataset, along with 78.48% and 44.93% for 3-class and 7-class classifications on the MPED dataset. These results highlight DGC-Link's potential for improved performance in EEG emotion recognition.

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.

Granger causality (GC) effective connectivity (EC) calculated from electroencephalogram (EEG) signals has been widely used in mental disorder detection. However, the existing methods only take into account linear dynamics or nonlinear dynamics within a single sample, ignoring the nonlinear dynamics shared by the same class of subjects. In this article, a model combining graph neural networks (GNNs) and variational autoencoders (VAEs) is proposed to construct shared latent nonlinear EC from raw EEG signals for depression detection. Several convolution modules and fully connected layers are used in the graph encoding network to learn the embeddings of the connectivity connected by every two EEG channels. In the graph decoding network, a class-specific Gaussian mixture model (GMM) is introduced in the VAEs to model shared dynamics in EC of the same class of subjects, and the shared dynamics combine the encoded embeddings of the EC and the past time series to restore raw EEG signals. Through a node-to-edge encoding process and an edge-to-node decoding process, the shared latent nonlinear EC in EEG signals can ultimately be learned by gradually optimizing the model's loss function. The performance of the proposed method is verified on several open-accessed datasets. The excellent results prove that the proposed neural networks can learn more generalized nonlinear EC representations, and shared latent dynamics discovery can also help to identify depression better. The code is available at https://github.com/william-yuan2012/DSLNEC-tscausality.

Stress has been identified as one of major causes of health issues. To detect the stress levels with higher accuracy, fusion of multimodal physiological signals is a promising technique. However, there is an asynchrony between physiological signals observed from different perspectives. Exploring the temporal alignment relationship between modalities is helpful to improve the quality of multimodal fusion. This paper proposes an end-to-end multimodal stress detection model based on Bidirectional Cross- and Self-modal Attention (BCSA) mechanism. Specifically, we first construct different feature extractors based on the characteristics of Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) to complete automated temporal feature extraction. Secondly, cross-modal attention is used to seek the alignment relationship between the two modalities and fully fuse cross-modal information. The self-modal attention is used to attenuate noise and redundant information, highlight important information and obtain salient stress representations. Finally, the stress representations of the two modalities are processed separately, and the mean square error (MSE) is used to narrow the gap between them. Experimental results on the UBFC-Phys dataset and WESAD dataset show that the proposed model can effectively improve the accuracy of stress recognition, and outperforms several state-of-the-art methods.

Remote photoplethysmography (rPPG)-based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, etc., in a noncontact manner. Existing mainstream convolutional neural network (CNN)-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native transformer-based framework called dual-path TokenLearner (dual-TL) is proposed in this article, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed dual-TL uses a spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The dual-TL achieves state-of-the-art performances in both intra and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement.

Domain adaptation (DA) has aroused a wide concern in electroencephalogram (EEG)-based cross-subject emotion recognition tasks. However, many existing DA algorithms focus more on transferability rather than discriminability. In addition, these algorithms typically rely on iterative optimization with pseudo-labels to attain the optimal model. In this study, a novel method with an online updating mechanism named discriminative joint knowledge transfer (DJKT) is proposed. A precise calculation of discriminative information for different emotional states within and across subjects is achieved by leveraging a small number of labeled target-domain samples. Furthermore, to accommodate the time-varying EEG, we extend the passive-aggressive (PA) algorithm to enable online adaptation of the emotion recognition model, thereby enhancing its suitability for real-world scenarios. Extensive experiments conducted on the SJTU emotion EEG dataset (SEED) and SEED-IV demonstrate the effectiveness of our approach. First, comprehensive incorporation of the discriminative information improves the performance of transfer learning significantly. In comparison with several state-of-the-art methods, DJKT exhibits significantly improved emotion recognition performance in both single-source to single-target (STS) and multisource to single-target (MTS) scenarios. Second, the online adjustment strategy effectively addresses the time-varying characteristics of EEG signals, leading to a more robust and stable model.

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.

Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.

Emotion recognition is the key to making machines more intelligent. This study proposes a novel sing-link end-to-end spatio-temporal demographic network (SSTD) that fuses spatial, temporal, and demographic information for electroencephalography (EEG)-based emotion recognition. In the SSTD model, an adaptive time window using single-link hierarchical clustering based on Riemannian metrics was realized for data preprocessing to solve the problem of individual differences. Then, the preprocessed EEG data acted as a gate recurrent unit (GRU) network input to calculate high-level time-domain features. At the same time, the EEG covariance matrices were fed into the symmetric positive definite matrix network (SPDNet) to calculate high-level spatial features. Given the correlation between EEG signals and individual demographic information, gender and age factors were integrated into the spatio-temporal model, resulting in more effective high-level features for EEG-based emotion recognition. Finally, extensive comparative experiments were conducted on two public datasets: DEAP and DREAMER. The average accuracy of valence and arousal on the DEAP dataset are 68.28% and 71.48%, respectively. The average accuracy of valence and arousal on the DREAMER dataset are 76.81% and 81.64%, respectively. Experimental results show that the SSTD model has an excellent recognition performance.

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.

Affective brain—computer interfaces have become an increasingly important topic to achieve emotional intelligence in human—machine collaboration. However, due to the complexity of electroencephalogram (EEG) signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of personality traits on emotional response, it would be helpful to integrate personality information and EEG signals for emotion recognition. This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for emotion recognition. Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in emotion recognition, a personality-guided regional-attention mechanism is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, personality traits, and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent emotion recognition and outperform state-of-the-art 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.

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

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.

Electroencephalogram(EEG) and eye movement have been extensively applied in the detection of anxiety disorders because they can reflect the brain functions and people's attentional bias. Although our previous work can make good use of the group structure information of EEG and eye movement signals, it mainly models the linear correlation and ignores the nonlinear correlation between two modalities. Therefore, we proposed kernel group sparse canonical correlation analysis (K-GSCCA) to study the nonlinear complex relationship and group structure information among EEG and eye movement features. Firstly, EEG signals were divided into 13 groups according to different brain regions, and eye movement signals were divided into 4 groups according to different visual behaviors. Then, we used the Gaussian kernel function to transform data into kernel space, effectively generated nonlinear cooperative fusion representation. The experimental outcomes demonstrated that K-GSCCA can be effective to solve the nonlinear correlation of group structure information between EEG and eye movement features. Using the support vector machine(SVM) classifier, we finally achieved the best classification accuracy of 87.47% in the fusion of the gamma band of EEG and eye movement features.

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.

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor. Our proposed approach 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. To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

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.

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.

In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.

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.

With the development of the electronic health record, sharing biomedical and healthcare data among heterogeneous systems has great potential to benefit both clinical healthcare and scientific research. Standardizing the data presentation is the best solution to exchange them among heterogeneous systems. However, electroencephalogram (EEG) data has met problems since existing diverse data formats are unsuitable for this purpose. To address this problem we introduce an XML-based data presentation method named eegML for EEG in this paper. Based on advantages of XML technologies, eegML is a generic system- and application-independent and flexible open solution to the effective presentation of EEG data. Besides, eegML could also be used to aid knowledge management and discovery in biomedical and healthcare informatics. This format, if widely adopted, could better promote EEG data management and mining.