International journal of neural systems最新文献_第10页

A Unified Hypergraph-Mamba Framework for Adaptive Electroencephalogram Modeling in Multi-view Seizure Prediction. 多视点癫痫发作预测自适应脑电图建模的统一超图-曼巴框架。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-10-07 DOI: 10.1142/S012906572550056X

Dengdi Sun, Yanqing Liu, Changxu Dong, Zongyun Gu

{"title":"A Unified Hypergraph-Mamba Framework for Adaptive Electroencephalogram Modeling in Multi-view Seizure Prediction.","authors":"Dengdi Sun, Yanqing Liu, Changxu Dong, Zongyun Gu","doi":"10.1142/S012906572550056X","DOIUrl":"10.1142/S012906572550056X","url":null,"abstract":"Seizure prediction from Electroencephalogram (EEG) signals is a critical task for proactive intervention in epilepsy management. Existing models often struggle to capture high-order inter-channel dependencies dynamically and adapt to the spectral variations preceding seizure onset, especially in cross-patient scenarios. To address these issues, a novel Unified Hypergraph-Mamba (UHM) framework, which for the first time integrates hypergraph-based spatial modeling with Mamba-based adaptive spectral modeling. Specifically, a hypergraph attention mechanism is designed to capture high-order spatial interactions among EEG channels, enabling dynamic representation of inter-channel dependencies. Concurrently, an adaptive spectral modeling module based on the Mamba architecture selectively emphasizes frequency components most indicative of preictal states. Together, these components form a unified architecture capable of jointly modeling spatiotemporal EEG dynamics. Extensive experiments conducted on both patient-specific and cross-patient settings demonstrate that our model consistently outperforms state-of-the-art baselines, achieving superior sensitivity and AUC.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550056"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Objective Assessment of Disorders of Consciousness Based on EEG Temporal and Spectral Features. 基于脑电图时间和频谱特征的意识障碍客观评估。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-09-22 DOI: 10.1142/S0129065725500674

Wanqing Dong, Yi Yang, Tong Wu, Xiaorong Gao, Yanfei Lin, Jianghong He

{"title":"Objective Assessment of Disorders of Consciousness Based on EEG Temporal and Spectral Features.","authors":"Wanqing Dong, Yi Yang, Tong Wu, Xiaorong Gao, Yanfei Lin, Jianghong He","doi":"10.1142/S0129065725500674","DOIUrl":"10.1142/S0129065725500674","url":null,"abstract":"Most existing studies analyzed the resting-state electroencephalogram (EEG) of DOC patients, and recent research demonstrated that the passive auditory paradigm was helpful for bedside detection of DOC and better captured sensory and cognitive responses. However, further studies of classification algorithms were needed for consciousness assessment in DOC based on task-state EEG data. In this study, EEG data from minimally conscious state (MCS) patients, vegetative state (VS) patients, and a healthy control group (HC) were collected using an auditory oddball paradigm. First, compared to the fragmented features adopted by most studies, multiple effective biomarkers for consciousness assessment in the time-frequency domains, connectivity and nonlinear dynamics were identified. Event-related potentials (ERP) results showed that MCS and VS patients exhibited lower N100 and MMN amplitudes than the HC group. Spectral analysis results indicated that VS patients had higher Delta power, and lower Alpha and Beta power than the MCS and HC groups. Second, different from insufficient classifiers in previous studies, this study systematically compared the performance of multiple machine learning and deep learning (DL) classifiers, including support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), eXtreme Gradient Boosting (XGBoost), decision tree (DT), EEGNet and ShallowConvNet. For machine learning methods, SVM and RF had an advantage in binary classification, and SVM had better performance in three-class classification. Among all individual classifiers, Shallow ConvNet had the best performance for binary and three-class classification. Moreover, an ensemble model incorporating all seven classifiers was proposed using a voting strategy, and further improved classification performance that was superior to existing studies. In addition, the importance of each feature was analyzed, identifying N100, MMN, Delta, Alpha, and Beta power as significant biomarkers of consciousness assessment.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550067"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Compliance Utilization Method Based on Adaptive Differential Privacy and Federated Learning. 基于自适应差分隐私和联邦学习的数据遵从性利用方法。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-08-30 DOI: 10.1142/S0129065725500601

Haiyan Kang, Bing Wu, Chong Zhang

{"title":"Data Compliance Utilization Method Based on Adaptive Differential Privacy and Federated Learning.","authors":"Haiyan Kang, Bing Wu, Chong Zhang","doi":"10.1142/S0129065725500601","DOIUrl":"10.1142/S0129065725500601","url":null,"abstract":"Federated learning (FL), as a method that coordinates multiple clients to train models together without handing over local data, is naturally privacy-preserving for data. However, there is still a risk that malicious attackers can steal intermediate parameters and infer the user's original data during the model training, thereby leaking sensitive data privacy. To address the above problems, we propose an adaptive differential privacy blockchain federated learning (ADP-BCFL) method to accomplish the compliant use of distributed data while ensuring security. First, utilize blockchain to accomplish secure storage and valid querying of user summary data. Second, propose an adaptive DP mechanism to be applied in the process of federal learning, which adaptively adjusts the threshold size of parameter tailoring according to the parameter characteristics, controls the amount of introduced noise, and ensures a good global model accuracy while effectively solving the problem of inference attack. Finally, the ADP-BCFL method was validated on the MNIST, Fashion MNIST datasets and spatiotemporal dataset to effectively balance model performance and privacy.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550060"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ERRATUM - Multi-Layer Feature Cascade Fusion Spiking Neural Network for Object Detection. 用于目标检测的多层特征级联融合尖峰神经网络。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1142/S0129065725920017

Yongqiang Ma, Bailin Guo, Xuetao Zhang

引用次数: 0

Lightweight Diffusion Models Based on Multi-Objective Evolutionary Neural Architecture Search. 基于多目标进化神经结构搜索的轻量级扩散模型。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-08-30 DOI: 10.1142/S0129065725500595

Yu Xue, Chunxiao Jiao, Yong Zhang, Ali Wagdy Mohamed, Romany Fouad Mansour, Ferrante Neri

{"title":"Lightweight Diffusion Models Based on Multi-Objective Evolutionary Neural Architecture Search.","authors":"Yu Xue, Chunxiao Jiao, Yong Zhang, Ali Wagdy Mohamed, Romany Fouad Mansour, Ferrante Neri","doi":"10.1142/S0129065725500595","DOIUrl":"10.1142/S0129065725500595","url":null,"abstract":"Diffusion models have achieved remarkable success in image generation, image super-resolution, and text-to-image synthesis. Despite their effectiveness, they face key challenges, notably long inference time and complex architectures that incur high computational costs. While various methods have been proposed to reduce inference steps and accelerate computation, the optimization of diffusion model architectures has received comparatively limited attention. To address this gap, we propose LDMOES (Lightweight Diffusion Models based on Multi-Objective Evolutionary Search), a framework that combines multi-objective evolutionary neural architecture search with knowledge distillation to design efficient UNet-based diffusion models. By adopting a modular search space, LDMOES effectively decouples architecture components for improved search efficiency. We validated our method on multiple datasets, including CIFAR-10, Tiny-ImageNet, CelebA-HQ [Formula: see text], and LSUN-church [Formula: see text]. Experiments show that LDMOES reduces multiply-accumulate operations (MACs) by approximately 40% in pixel space while outperforming the teacher model. When transferred to the larger-scale Tiny-ImageNet dataset, it still generates high-quality images with a competitive FID score of 4.16, demonstrating strong generalization ability. In latent space, MACs are reduced by about 50% with negligible performance loss. After transferring to the more complex LSUN-church dataset, the model surpasses baselines in generation quality while reducing computational cost by nearly 60%, validating the effectiveness and transferability of the multi-objective search strategy. Code and models will be available at https://github.com/GenerativeMind-arch/LDMOES.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550059"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physiological Response in Children with Autism Spectrum Disorder (ASD) During Social Robot Interaction. 自闭症谱系障碍儿童在社交机器人互动中的生理反应。

IF 6.4

International journal of neural systems Pub Date : 2025-12-30 Epub Date: 2025-12-06 DOI: 10.1142/S0129065725500662

Gema Benedicto-Rodríguez, Andrea Hongn, Carlos G Juan, Javier Garrigós-Guerrero, María Paula Bonomini, Eduardo Fernandez-Jover, Jose Manuel Ferrández-Vicente

{"title":"Physiological Response in Children with Autism Spectrum Disorder (ASD) During Social Robot Interaction.","authors":"Gema Benedicto-Rodríguez, Andrea Hongn, Carlos G Juan, Javier Garrigós-Guerrero, María Paula Bonomini, Eduardo Fernandez-Jover, Jose Manuel Ferrández-Vicente","doi":"10.1142/S0129065725500662","DOIUrl":"10.1142/S0129065725500662","url":null,"abstract":"In a world where social interaction presents challenges for children with Autism Spectrum Disorder (ASD), robots are stepping in as allies in emotional learning. This study examined how affective interactions with a humanoid robot elicited physiological responses in children with ASD, using electrodermal activity (EDA) and heart rate variability (HRV) as key indicators of emotional arousal. The objectives were to identify emotionally salient moments during human-robot interaction, assess whether certain individual characteristics - such as age or ASD severity - modulate autonomic responses, and evaluate the usefulness of wearable devices for real-time monitoring. Thirteen children participated in structured sessions involving a range of social, cognitive, and motor tasks alongside the robot Pepper. The results showed that the hugging phase (HS2) often generated greater autonomic reactivity in children, especially among younger children and those with higher levels of restlessness or a higher level of ASD. Children with level 2 ASD displayed higher sympathetic activation compared to level 1 participants, who showed more HRV stability. Age also played a role, as younger children demonstrated lower autonomic regulation. These findings highlight the relevance of physiological monitoring in detecting emotional dysregulation and tailoring robot-assisted therapy. Future developments will explore adaptive systems capable of adjusting interventions in real time to better support each child's unique needs.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550066"},"PeriodicalIF":6.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications. 一种可解释的脑机接口EEG解码3d深度学习模型。

IF 6.4

International journal of neural systems Pub Date : 2025-12-30 Epub Date: 2025-10-18 DOI: 10.1142/S012906572550073X

Muhammad Suffian, Cosimo Ieracitano, Francesco C Morabito, Nadia Mammone

{"title":"An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.","authors":"Muhammad Suffian, Cosimo Ieracitano, Francesco C Morabito, Nadia Mammone","doi":"10.1142/S012906572550073X","DOIUrl":"10.1142/S012906572550073X","url":null,"abstract":"Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming and limit the application of deep learning approaches, due to general need of large amount of data to properly train these models. In this context, this paper proposes a multidimensional and explainable deep learning framework for fast and interpretable EEG decoding. In particular, EEG signals are projected into the spatial-spectral-temporal domain and processed using a custom three-dimensional (3D) Convolutional Neural Network, here referred to as EEGCubeNet. In this work, the method has been validated on EEGs recorded during motor BCI experiments. Namely, hand open (HO) and hand close (HC) movement planning was investigated by discriminating them from the absence of movement preparation (resting state, RE). The proposed method is based on a global- to subject-specific fine-tuning. The model is globally trained on a population of subjects and then fine-tuned on the final user, significantly reducing adaptation time. Experimental results demonstrate that EEGCubeNet achieves state-of-the-art performance (accuracy of [Formula: see text] and [Formula: see text] for HC versus RE and HO versus RE, binary classification tasks, respectively) with reduced framework complexity and with a reduced training time. In addition, to enhance transparency, a 3D occlusion sensitivity analysis-based explainability method (here named 3D xAI-OSA) that generates relevance maps revealing the most significant features to each prediction, was introduced. The data and source code are available at the following link: https://github.com/AI-Lab-UniRC/EEGCubeNet.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550073"},"PeriodicalIF":6.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subthalamic Nucleus Deep Brain Stimulation Modulates Auditory Steady State Responses in Parkinson's Disease. 丘脑下核深部脑刺激调节帕金森病的听觉稳态反应。

IF 6.4

International journal of neural systems Pub Date : 2025-12-30 Epub Date: 2025-12-09 DOI: 10.1142/S0129065725500820

Thomas Pirenne, Mansoureh Fahimi Hnazaee, Patrick Santens, Aline Moorkens, Marc M Van Hulle

{"title":"Subthalamic Nucleus Deep Brain Stimulation Modulates Auditory Steady State Responses in Parkinson's Disease.","authors":"Thomas Pirenne, Mansoureh Fahimi Hnazaee, Patrick Santens, Aline Moorkens, Marc M Van Hulle","doi":"10.1142/S0129065725500820","DOIUrl":"https://doi.org/10.1142/S0129065725500820","url":null,"abstract":"Deficits in auditory perception have been widely observed in Parkinson's disease (PD) patients and the literature attributes it, in part, to impaired central auditory processing. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established therapeutic option for patients with advanced PD. Analysis of auditory evoked potentials suggested a modulatory effect of DBS on central auditory processing. To better understand the latter, we investigated whether DBS modulates auditory steady state responses (ASSR) in electroencephalography (EEG) recordings of 5 PD patients. ASSRs are neural responses along central auditory pathways phase-locked to an auditory stimulus which can serve to understand the spectral aspects of central auditory processing. In our analyses, we estimate the intensity of ASSRs with a novel method based on canonical correlation analysis (CCA) and compare them in DBS ON and OFF conditions. Our results suggest that DBS effectively reduces ASSR in patients with PD. A comparison to age-matched healthy participants suggests a pathological effect of PD on ASSRs, which is disrupted by DBS. These findings support our hypothesis that DBS suppresses central auditory processing. Further research is required to assess the symptomatic effect of this modulation, as well as which cortical and subcortical generators are most affected. A better understanding of the auditory side-effects of DBS could lead to improved treatment options.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 13","pages":"2550082"},"PeriodicalIF":6.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145764781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms. 具有综合注意机制的复合眼启发的多尺度神经结构。

IF 6.4

International journal of neural systems Pub Date : 2025-12-30 Epub Date: 2025-09-18 DOI: 10.1142/S0129065725500650

Ferrante Neri, Mengchen Yang, Yu Xue

{"title":"A Compound-Eye-Inspired Multi-Scale Neural Architecture with Integrated Attention Mechanisms.","authors":"Ferrante Neri, Mengchen Yang, Yu Xue","doi":"10.1142/S0129065725500650","DOIUrl":"10.1142/S0129065725500650","url":null,"abstract":"In the context of neural system structure modeling and complex visual tasks, the effective integration of multi-scale features and contextual information is critical for enhancing model performance. This paper proposes a biologically inspired hybrid neural network architecture - CompEyeNet - which combines the global modeling capacity of transformers with the efficiency of lightweight convolutional structures. The backbone network, multi-attention transformer backbone network (MATBN), integrates multiple attention mechanisms to collaboratively model local details and long-range dependencies. The neck network, compound eye neck network (CENN), introduces high-resolution feature layers and efficient attention fusion modules to significantly enhance multi-scale information representation and reconstruction capability. CompEyeNet is evaluated on three authoritative medical image segmentation datasets: MICCAI-CVC-ClinicDB, ISIC2018, and MICCAI-tooth-segmentation, demonstrating its superior performance. Experimental results show that compared to models such as Deeplab, Unet, and the YOLO series, CompEyeNet achieves better performance with fewer parameters. Specifically, compared to the baseline model YOLOv11, CompEyeNet reduces the number of parameters by an average of 38.31%. On key performance metrics, the average Dice coefficient improves by 0.87%, the Jaccard index by 1.53%, Precision by 0.58%, and Recall by 1.11%. These findings verify the advantages of the proposed architecture in terms of parameter efficiency and accuracy, highlighting the broad application potential of bio-inspired attention-fusion hybrid neural networks in neural system modeling and image analysis.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550065"},"PeriodicalIF":6.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consensus-Based 3D View Generation from Noisy Images. 基于共识的噪声图像三维视图生成。

IF 6.4

International journal of neural systems Pub Date : 2025-12-30 Epub Date: 2025-08-22 DOI: 10.1142/S0129065725500571

José A Rodríguez-Rodríguez, Miguel A Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio

{"title":"Consensus-Based 3D View Generation from Noisy Images.","authors":"José A Rodríguez-Rodríguez, Miguel A Molina-Cabello, Rafaela Benítez-Rochel, Ezequiel López-Rubio","doi":"10.1142/S0129065725500571","DOIUrl":"10.1142/S0129065725500571","url":null,"abstract":"The real-time synthesis of 3D views, facilitated by convolutional neural networks like NeX, is increasingly pivotal in various computer vision applications. These networks are trained using photographs taken from different perspectives during the training phase. However, these images may be susceptible to contamination from noise originating from the vision sensor or the surrounding environment. This research meticulously examines the impact of noise on the resulting image quality of 3D views synthesized by the NeX network. Various noise levels and scenes have been incorporated to substantiate the claim that the presence of noise significantly degrades image quality. Additionally, a new strategy is introduced to improve image quality by calculating consensus among NeX networks trained on images pre-processed with a denoising algorithm. Experimental results confirm the effectiveness of this technique, demonstrating improvements of up to 1.300 dB and 0.032 for Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), respectively, under certain scenes and noise levels. Notably, the performance gains are especially significant when using synthesized images generated by NeX from noisy inputs in the consensus process.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550057"},"PeriodicalIF":6.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0