As the prevalence of depression continues to rise, the timely and accurate recognition of its early signs is crucial for effective prevention and intervention. However, current clinical diagnostic methods are limited by the absence of objective biomarkers and inefficiencies in early recognition. Recent research has revealed a significant correlation between gait patterns and depression risk, suggesting that gait analysis could serve as a promising tool for early diagnosis. Depression-associated gait characteristics are defined by two key aspects: (1) they are dynamic, reflecting temporal abnormalities in movement, and (2) they manifest across both localized body regions and broader global movement patterns of the body. Based on these insights, we propose a novel Spatio-temporal Multi-granularity Network (STM-Net) for depression risk recognition. In the temporal domain, we present a Multi-grain Temporal Focus (MTF) module, designed to capture the rich dynamic temporal information embedded in the gait cycle of individuals with depression. In the spatial domain, we introduce a Multi-grain Spatial Focus (MSF) module, which effectively captures spatial features and their interactions in depression-related body regions through joint-level and part-level attention mechanisms. Extensive experimental results demonstrate that STM-Net achieves state-of-the-art performance on a large open-source dataset.

Stereoscopic 3D motion videos offer an immersive experience but can induce visual discomfort. Existing research primarily focuses on assessing binocular visual discomfort using physiological signals like EEG, with limited attention to motion-induced discomfort. Studies on motion-induced discomfort in 3D videos typically rely on subjective evaluations. However, due to dynamic changes in parallax, relying solely on subjective ratings makes it difficult to accurately capture the variations in discomfort during viewing. To address this challenge, we introduce the first subjective-objective EEG dataset for assessing stereoscopic visual discomfort induced by 3D motion videos, which includes real-time EEG signals and subjective discomfort ratings. This dataset outperforms existing related EEG datasets in diversity of motion speeds, directions, and subject numbers. We analyzed the discomfort trends across different motion speeds and explored the correlation between objective EEG signals and subjective experiences through the analysis of average electrode functional connectivity. Results show that motion speed significantly impacts comfort, with EEG-based measures proving more reliable than subjective ratings. Additionally, experiments with various algorithms validate the dataset’s effectiveness, laying the foundation for future deep learning research on motion-induced stereoscopic discomfort.

Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.

Research on emotion recognition in conversations emphasises the importance of complex relationships between conversational context and multimodality. Graph-based methods, particularly hypergraph-based methods have shown promise in capturing these relationships. However, challenges persist in avoiding redundant context while capturing essential information for optimal context embeddings and fully leveraging cross-modal complementarities for sufficient fusion. In contrast, the human brain flexibly retrieves relevant memories and integrates multi-modal data for accurate recognition. Based on this superiority, we propose BrainyHGNN, a brain-inspired hypergraph neural network. It integrates a Dynamic Memory Selector for contextual hyperedges, mimicking selective memory retrieval mechanisms for adaptive and modality-specific context retrieval. HierSensNet is designed for multi-modal hyperedges, mirroring hierarchical cross-modal interaction mechanisms to ensure effective multimodal fusion. Experimental results on two benchmark datasets validate the superior performance of BrainyHGNN, confirming the effectiveness of its innovative approach. This work highlights the potential of brain-inspired methods to advance flexible context retrieval and sufficient multimodal fusion, presenting a promising direction for future research in this domain.

Motion planning, considered a fuzzy sequential decision-making problem, encounters significant challenges due to inherent environmental uncertainty. Traditional planning methods that rely on single strategies often struggle in complex scenarios. While fuzzy systems excel at handling uncertainty, high-dimensional continuous spaces require a large number of fuzzy rules, which significantly increases computational complexity. In contrast, humans leverage limited and fuzzy information to address various decision-making scenarios flexibly and efficiently. The concurrent reasoning mechanism in the prefrontal cortex plays a crucial role during this process. Consequently, the brain-inspired model and the concept of multiple fuzzy rules offer a novel perspective for the above issues. Motivated by these insights, this article proposes a brain-inspired motion planning method called harmonized learning with concurrent arbitration (HLCA). Specifically, inspired by the concurrent inference model, a concurrent arbitration module is employed in the planning process to effectively manage the boundary between exploration and exploitation. Furthermore, inspired by the multistrategy processing mechanism, HLCA introduces multistrategy harmonized learning by referring to the mechanism for operating multiple fuzzy rules, allowing the dynamic selection of strategies through a reliability function to enable self-improving learning. Experimental results demonstrate that HLCA outperforms state-of-the-art benchmarks, highlighting its potential to enhance the planning performance of robots by learning from the human brain.

Intermittent theta burst stimulation (iTBS) is a non-invasive technique frequently employed to induce neural plastic changes and enhance visual attention. Currently, most studies utilized a single iTBS session on healthy subjects to induce short-term neural plastic changes within tens of minutes post-stimulation and investigate its single-session effect on attention performance. Few studies have conducted multiple iTBS sessions on the cerebellum to explore long-term effects on the cerebral cortex and daily effects on visual attention performance. In this study, 18 healthy subjects were involved in a randomized, sham-controlled experiment over one week. All the subjects received daily session of bilateral cerebellar Crus II iTBS or sham stimulation and completed a visual search task. Resting-state electroencephalogram (EEG) was collected 48 hours pre- and post-experiment to assess plastic changes induced by iTBS. The results indicated that the iTBS group exhibited higher accuracy and lower time costs than the sham group after three sessions of iTBS. In addition, iTBS-induced plastic changes persisted up to 48 hours post-experiment, including left-shifted individual alpha frequency, increased intrinsic excitability (the likelihood that a neuron will generate an output in response to a given input), and enhanced PLV functional connectivity (phase synchronization between different brain region). Furthermore, we found that cerebellar iTBS induced a remote effect on the frontal region. Our study revealed the capacity of cerebellar Crus II iTBS to induce plastic changes and enhance attention performance, providing a potential avenue for using iTBS to promote rehabilitation.

Recently, diffusion-based methods for monocular 3D human pose estimation have achieved state-of-the-art (SOTA) performance by directly regressing the 3D joint coordinates from the 2D pose sequence. Although some methods decompose the task into bone length and bone direction prediction based on the human anatomical skeleton to explicitly incorporate more human body prior constraints, the performance of these methods is significantly lower than that of the SOTA diffusion-based methods. This can be attributed to the tree structure of the human skeleton. Direct application of the disentangled method could amplify the accumulation of hierarchical errors, propagating through each hierarchy. Meanwhile, the hierarchical information has not been fully explored by the previous methods. To address these problems, a Disentangled Diffusion-based 3D human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose. In our approach: (1) We disentangle the 3d pose and diffuse the bone length and bone direction during the forward process of the diffusion model to effectively model the human pose prior. A disentanglement loss is proposed to supervise diffusion model learning. (2) For the reverse process, we propose Hierarchical Spatial and Temporal Denoiser (HSTDenoiser) to improve the hierarchical modelling of each joint. Our HSTDenoiser comprises two components: the Hierarchical-Related Spatial Transformer (HRST) and the Hierarchical-Related Temporal Transformer (HRTT). HRST exploits joint spatial information and the influence of the parent joint on each joint for spatial modeling, while HRTT utilizes information from both the joint and its hierarchical adjacent joints to explore the hierarchical temporal correlations among joints. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the SOTA disentangled-based, non-disentangled based, and probabilistic approaches by 10.0%, 2.0%, and 1.3%, respectively.

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet, the first dataset tailored to the preferences of young audiences, enabling the generation of aesthetically oriented dances. And it surpasses the${\it AIST}++{\it dataset}$in music genre di-versity and the intricacy and depth of dance movements. Moreover, the proposed POPDG model within the iD-DPMframework enhances dance diversity and, through the Space Augmentation Algorithm, strengthens spatial physi-cal connections between human body joints, ensuring that increased diversity does not compromise generation qual-ity. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and mu-sic. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore, the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luol/POPDG.

Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-based GNNs to learn the topological structure of brain networks. Graph rewiring methods and curvature GNNs have been proposed to alleviate over-squashing. However, most graph rewiring methods overlook node features and curvature GNNs neglect the geometric properties of signed curvature. In this study, a Signed Curvature GNN (SCGNN) was proposed to rewire the graph based on node features and curvature, and learn the representation of signed curvature. First, a Mutual Information Ollivier-Ricci Flow (MORF) was proposed to add connections in the neighborhood of edge with the minimal negative curvature based on the maximum mutual information between node features, improving the efficiency of information interaction between nodes. Then, a Signed Curvature Convolution (SCC) was proposed to aggregate node features based on positive and negative curvature, facilitating the model's ability to capture the complex topological structures of brain networks. Additionally, an Ollivier-Ricci Gradient Pooling (ORG-Pooling) was proposed to select the key nodes and topology structures by curvature gradient and attention mechanism, accurately obtaining the global representation for brain age estimation. Experiments conducted on six public datasets with structural magnetic resonance imaging (sMRI), spanning ages from 18 to 91 years, validate that our method achieves promising performance compared with existing methods. Furthermore, we employed the gaps between brain age and chronological age for identifying Alzheimer's Disease (AD), yielding the best classification performance.

Visual discomfort assessment in stereoscopic three-dimensional (3-D) electroencephalography (EEG) data is challenging. The SOTA research applies deep neural networks to learn temporal information in continuous EEG signals and global spatial information about electrode locations. This work makes the first attempt to jointly deeply model spatio-temporal coupling relationships and select strong dependencies in 3-D EEG data. We explore whether modeling such temporal and spatial dependencies would improve visual discomfort assessment. To address these issues, this work introduces multidilation temporal and spatial dependence-based convolutional neural networks (MTSD) to explore spatio-temporal couplings by 1) learning hierarchical temporal relations within both continuous and interval data and 2) learning and selecting strong spatial dependencies between electrodes (their locations). MTSD captures 1) multidilation temporal relations in continuous and interval receptive fields by a parallel convolution module and 2) spatial dependencies between electrodes in stereoscopic 3-D EEG data by a constrained self-attention module with a copula-based variable dependence strength filter for visual discomfort assessment. Experiments compare five EEG-based deep networks and three MTSD variants. MTSD makes improvement in discriminating visual discomfort by capturing strong spatio-temporal couplings but uses significantly less computational resources.

Anxiety is a complex emotional state that has a great impact on people's physical and mental health. Effectively identifying different anxiety states is very important. By inducing various anxiety states of 12 healthy college students with electroencephalograph (EEG) recording, comprehensive EEG features, including not only commonly used frequency domain features but also the time domain, statistical and nonlinear features were extracted from different EEG bands and brain locations. Next, correlation analysis was performed between various features and anxiety level changes that were predetermined at each stage of the experiment using a 5-point Likert scale, and the most relevant features were collected. Then, different classifiers were applied to classify four anxiety levels using different features alone or together to explore their anxiety recognition ability. Based on our dataset, the highest accuracy of identifying four anxiety states reached approximately 62.56 percent using the Support Vector Machine (SVM), which improved the classification accuracy compared with previous studies. The results also revealed the importance of EEG linear features (especially for features including total power, mean square and variance) in anxiety recognition. Furthermore, it suggested that EEG features in the beta band and the frontal lobe contributed to anxiety recognition more than the features in the other bands or other brain locations. In short, this study improves the accuracy of multi-level anxiety recognition and helps in choosing better features for anxiety recognition, which lay the foundation for the detection of continuous anxiety changes.

In this article, we investigate brain directed connectivity (BDC) networks for emotion recognition using electroencephalogram (EEG) source signals that were estimated from high-density sensor EEG signals, for the first time. Currently, a variety of features extracted from sensor EEG signals are used for emotion recognition. However, they cannot unambiguously describe the location of emotions associated with neural activities and information propagation or the interaction between brain regions. In addition, most current studies use low-density sensor EEG signals. Moreover, source signals estimated from high-density sensor EEG signal have not been employed for emotion recognition to date. We designed a BDC network-based framework using EEG source signals to investigate emotion recognition. The global cortex factor-based multivariate autoregressive (GCF-MVAR) method was utilized to extract emotion-related BDC features. Our study revealed that the combined BDC and DE features facilitated a recognition accuracy of up to 89.58 percent, which is higher than the rate obtained from BDC features and DE features alone. The sensor features derived from high-density EEG signals also exhibited higher recognition accuracy compared to low-density EEG signals. These findings suggest that BDC features derived from EEG source signals can better characterize human emotional states and are meaningful for emotion recognition.

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (pHC≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.

Neuroimaging studies of cognitive learning have identified the important roles of Cortico-Basal ganglia- Cerebellar (CBC) loops, and the neurofeedback training based on real-time functional magnetic resonance imaging (rt-fMRI) has been deemed as a kind of cognitive learning. However, how the connectivity in CBC loops change during the feedback training and the underlying learning mechanism behind the training both remain unclear. In this paper, we firstly used Granger causality model method to construct CBC loops in a working memory feedback training task by rt-fMRI. Then, we examined the interaction changes in CBC loops induced the training. The results showed that the connectivity of fronto- parietal, cortico-basal ganglia (BG) and cortico-cerebellar in CBC loops were significantly enhanced during the training in the experimental group. Further correlation analysis indicated the connectivity changes of cortico-BG were stronger positively correlated with the behavioral improvements. These findings suggest that the interaction between the cortex and BG in the feedback training is an essential factor to the behavioral improvement which makes the individual to complete cognitive learning better.

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

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.

Independent component analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components (ICs) in fMRI data is critical to reduce over/underfitting. Various methods based on information theoretic criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data. An important assumption of ITC is that the noise is purely white. However, this assumption is often violated by the existence of temporally correlated noise in fMRI data. In this study, we introduced a filtering method into the order selection to remove the autocorrelation from the colored noise by using the whitening filter proposed by Prudon and Weisskoff. Results of the simulated data show that the filtering method has strong robustness to noise and significantly improves the accuracy of order selection from data with colored noise. Moreover, the multifiltering method proposed by us was applied to real fMRI data to improve the performance of ITC. Results of the real fMRI data show that the proposed method can alleviate the overestimation due to the autocorrelation of colored noise. We further compared the stability of IC estimates of real fMRI data at order estimated by minimum description length criterion based on the filtered and unfiltered data by using the software package ICASSO. Results show that ICA yields more stable IC estimates using the reduced order by filtering.

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option. BCI can help users send messages and commands to the external world without using their brain's normal output channels or muscles. The central element in each BCI system is to find a reliable method to detect the specific feature patterns extracted from the raw brain signals, and then translate it into usable control signals. In this paper, we introduce our approach to detect N170 component of the event-related brain potential (ERP) based on its spatiotemporal patterns in single-trial EEG signals. Common spatial pattern (CSP) method and machine-learning technique support vector machine (SVM) are adopted for N170 feature extraction and translation, i.e. they convert electrophysiological input from the user into on-off signal to control external devices. Our results indicate that the CSP can effectively extract discriminatory information, and SVM has an efficient performance for N170 classification. Comparing to several other methods, high performances of our framework show that the temporal and spatial features of N170 are very stable and it is promising for new type BCI applications.