Electroencephalogram (EEG) individual differences are a critical factor influencing EEG-based emotion recognition, yet they have not been thoroughly investigated, hindering the development of affective Brain-Computer Interfaces (aBCI). Facing the lack of EEG information decoding research, we conducted an interpretable study on EEG individual differences using five datasets (SEED, SEED-IV, SEED-V, RCLS, and MPED). We analyzed the impact of different EEG information (individual, session, emotion, and trial) through sample space visualization, aggregation phenomena quantification, and energy pattern analysis. By examining emotional difference feature distribution patterns, we identified the Cross-Session Consistency of Individual Emotional Patterns (CCIEP) and the Individual Emotional Pattern Difference (IEPD). These characteristics are the main factors impacting emotion recognition stability. To quantify emotional patterns, we proposed the Correction T-test (CT) weight extraction method. Leveraging individual emotional pattern and trial information, we developed the Weight-based Channel-model Matrix Framework (WCMF) to address limitations of traditional modeling approaches caused by IEPD. Finally, WCMF was validated on cross-dataset tasks through two practical scenario experiments. The results demonstrated that WCMF achieves more stable and superior performance compared to traditional methods. This study provides a deeper understanding of EEG individual differences and offers a robust framework to advance aBCI systems.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses a significant and growing global health challenge due to its multifactorial origin and complex pathophysiological mechanisms. Modern computational biology provides powerful tools to uncover risk factors from large-scale biomedical datasets, yet distinguishing causality from correlation within such observational data remains a fundamental methodological bottleneck. To address this issue, we implement a two-sample Mendelian randomization (MR) computational framework, employing genetic variants as instrumental variables to infer causality from complex biological traits. This study focuses on evaluating potential causal relationships among key biological markers, specifically Apolipoprotein E4 (ApoE4), brain-derived neurotrophic factor (BDNF), and AD risk. The primary causal inference was conducted using the inverse-variance weighted (IVW) estimator, with a suite of sensitivity analyses, including Mendelian randomization-Egger regression (MR-Egger), weighted median, and Mendelian randomization pleiotropy RESidual sum and outlier (MR-PRESSO) coupled with leave-one-out validation to ensure methodological robustness and mitigate confounding bias. The computational results indicate a statistically significant negative causal effect of genetically instrumented ApoE4 exposure on BDNF levels, suggesting a potential pathway through which ApoE4 influences AD susceptibility. Sensitivity analyses consistently supported the robustness and validity of this causal inference. This work presents a rigorous, data-driven computational pipeline for causal discovery from high-dimensional biomedical data, offering a generalizable methodological framework that can be extended to infer causal networks in other multifactorial disorders.

As a crucial component of intelligent transportation systems (ITS), accurate traffic flow prediction has attracted considerable attention. Numerous popular models, such as recurrent neural networks (RNN) and graph convolution networks (GCN), have been extensively applied to this task. However, due to the constraints of the topology of urban road network and the law of dynamic change with time, the single-layer network model cannot capture the dynamic correlation between the spatial–temporal and the traditional road network characteristics for traffic flow. In this article, we propose a spatial–temporal twolayer synchronous network (ST2SNet) for traffic flow prediction. First, we introduce a novel network structure comprising a spatial–temporal fusion layer and a situation awareness layer, where temporal and spatial correlations are fused synchronously using a transformer architecture. Next, we incorporate the residual gated GCN structure and a 2-D convolution network to effectively integrate road network information and temporal trends for spatial–temporal traffic flow prediction. Finally, we evaluate ST2SNet on two benchmark datasets, PeMSD4, and PeMSD8. Compared to baseline methods, our model achieves MAE improvements ranging from 4.00% to 48.30% on PeMSD4 and from 4.88%to 54.13%on PeMSD8. These outstanding experimental results demonstrate that ST2SNet significantly enhances prediction performance.

The realization process of brain cognitive and emotional functions involves the dynamic integration and coordination across multiple time scales. Simultaneously, anomalous topological structures in higher-order brain network have been founded as important characteristics for depression identification. To capture the high-order dynamic temporal characteristics of the brain for depression group across time scales, this study proposed a method for analyzing the high-order brain network in depression based on dynamic functional connectivity. Firstly, a dynamic functional connectivity matrix was constructed to obtain the temporal sequences of functional connectivity. Secondly, a hyper-network based on LASSO method was constructed using functional connectivity as nodes to explore the topological structure of the brain functional network. The results revealed the presence of anomalous long-range connections between brain regions in the depression group, particularly concentrated within the Dorsal Attention Network(DAN) and the Default Mode Network(DMN). These connections were mainly distributed in the frontal, temporal, and parietal regions. In terms of network metrics analysis, the clustering coefficient of the depression group was significantly lower than that of the normal group, especially in hyper-network clustering coefficients 2(HCC2). This suggested a weakened information connection between brain regions in the depression group. Furthermore, the experiment also found that the selection of hyper-network parameters had a significant impact on the network structure. In summary, this study demonstrated the feasibility of exploring the anomalous topological structure of the brain using the concept of the high-order network dynamics reconstruction for depression patients, and provided new insights into the construction of brain functional networks.

Major depressive disorder (MDD) may be driven by dysfunction in intrinsic dynamic properties of the brain, and EEG microstate is a promising method for analyzing brain dynamics. However, the alterations in EEG microstate is still not entirely clear, and its ability for MDDs detection is worth probing. Moreover, the mechanism behind the neural networks contributing to microstates remains poorly understood in MDDs. Therefore, we applied microstate analysis and Topographic Electrophysiological State Source-imaging (TESS) on EEG data of 27 MDDs and 28 healthy controls (HCs). Compared to HCs, MDDs had apparent increase in microstate C and decrease in microstate D. Furthermore, TESS results showed that the underlying network of microstate C in MDDs overlapped with the anterior cingulate cortex and left insula gyrus, whereas main source of microstate D was in the orbital part of inferior frontal gyrus. The reduced transition probability from C to D in MDDs may reveal an imbalance between the networks of microstates. The microstate parameters as features reached good performance in identifying MDD (89.09% accuracy, 92.86% sensitivity, 85.19% specificity), indicating their potential as biomarkers of depression pathology. Collectively, these results highlight alteration of brain activity patterns and provide new insights into abnormal EEG dynamics in MDDs.

OBJECTIVE: Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. METHODS: We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. RESULTS: The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs); Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs; Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects; The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. SIGNIFICANCE: Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.