International journal of neural systems最新文献_第2页

Interactive EEG Emotion Recognition with Incremental Gaussian Processes. 基于增量高斯过程的交互式脑电情感识别。

International journal of neural systems Pub Date : 2025-05-24 DOI: 10.1142/S0129065725500418

Xiangle Ping, Wenhui Huang

{"title":"Interactive EEG Emotion Recognition with Incremental Gaussian Processes.","authors":"Xiangle Ping, Wenhui Huang","doi":"10.1142/S0129065725500418","DOIUrl":"https://doi.org/10.1142/S0129065725500418","url":null,"abstract":"Interactivity is crucial for enabling models to adjust and optimize based on user feedback, thereby enhancing overall performance. However, existing electroencephalogram (EEG)-based emotion recognition models rely on static training paradigms, lack interactivity, and struggle to effectively handle uncertainty in predictions. To address this issue, we propose a novel paradigm for interactive emotion recognition based on incremental Gaussian processes (GP). Unlike existing methods, our approach introduces an expert interaction mechanism to correct samples with high predictive uncertainty and incrementally update the model accordingly, thereby optimizing its performance. First, we model the emotion recognition task as a GP-based framework, utilizing the variance of the GP to quantify the model's uncertainty, thereby guiding experts in targeted interactions. Second, within the GP framework, we propose a novel incremental update strategy that allows the GP to incrementally update prediction results and uncertainties based only on new data obtained through expert interactions, without reprocessing all existing data. This effectively overcomes the shortcomings of traditional GP in updating efficiency. Third, to address the high computational complexity of GP, we use a sparse approximation strategy, selecting inducing points and performing variational inference to efficiently approximate the GP posterior, thereby reducing computational complexity. Subject-dependent and subject-independent experiments conducted on the DEAP and DREAMER datasets demonstrate that the proposed method exhibits significant advantages over state-of-the-art (SOTA) methods. In subject-dependent experiments, our method achieved the highest improvement (1.73%) in the Dominance dimension on the DREAMER dataset. In subject-independent experiments, it attained the largest performance improvement (2.96%) in the Arousal dimension on the DEAP dataset. These results further validate the proposed method's effectiveness.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550041"},"PeriodicalIF":0.0,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144145249","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

Nonlinear Spiking Neural Systems for thermal Image Semantic Segmentation Networks. 热图像语义分割网络的非线性尖峰神经系统。

International journal of neural systems Pub Date : 2025-05-19 DOI: 10.1142/S0129065725500388

Peng Wang, Minglong He, Hong Peng, Zhicai Liu

{"title":"Nonlinear Spiking Neural Systems for thermal Image Semantic Segmentation Networks.","authors":"Peng Wang, Minglong He, Hong Peng, Zhicai Liu","doi":"10.1142/S0129065725500388","DOIUrl":"https://doi.org/10.1142/S0129065725500388","url":null,"abstract":"Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by restoring details and contextual information. However, the spatial discrepancy between different modalities in RGB-Thermal (RGB-T) semantic segmentation tasks complicates the process of multimodal feature fusion, leading to a loss of spatial contextual information and limited model performance. This paper proposes a channel-space fusion nonlinear spiking neural P system model network (CSPM-SNPNet) to address these challenges. This paper designs a novel color-thermal image fusion module to effectively integrate features from both modalities. During decoding, a nonlinear spiking neural P system is introduced to enhance multi-channel information extraction through the convolution of spiking neural P systems (ConvSNP) operations, fully restoring features learned in the encoder. Experimental results on public datasets MFNet and PST900 demonstrate that CSPM-SNPNet significantly improves segmentation performance. Compared with the existing methods, CSPM-SNPNet achieves a 0.5% improvement in mIOU on MFNet and 1.8% on PST900, showcasing its effectiveness in complex scenes.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550038"},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096588","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

Enhanced Graph Attention Network by Integrating Transformer for Epileptic EEG Identification. 集成变压器的增强图注意网络用于癫痫脑电识别。

International journal of neural systems Pub Date : 2025-05-09 DOI: 10.1142/S0129065725500376

Zhenhua Xie, Jian Lian, Dong Wang

{"title":"Enhanced Graph Attention Network by Integrating Transformer for Epileptic EEG Identification.","authors":"Zhenhua Xie, Jian Lian, Dong Wang","doi":"10.1142/S0129065725500376","DOIUrl":"https://doi.org/10.1142/S0129065725500376","url":null,"abstract":"Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing the complex dynamics of Electroencephalogram (EEG) signals and generalizing across diverse patient populations. In this study, the graph attention network and the transformer model are integrated for EEG signal classification, leveraging the enhanced capability to dynamically compute attention weights and adapt to the variable relevance of brain regions. The proposed approach is capable of modeling the intricate relationships within EEG activities by learning context-dependent attention scores. We conducted a comprehensive evaluation of the proposed approach comparing with the state-of-the-art algorithms. Experimental outcomes show that it surpasses the competing models. The superior performance is attributed to the proposed approach's dynamic attention mechanism, which better captures the nuanced patterns in EEG signals across different subjects and seizure types. In the experiments, the CHB-MIT dataset was exploited, which served as a benchmark for evaluating the performance of the proposed framework in distinguishing interictal, ictal, and normal EEG patterns. The results prove the usefulness of our work in advancing EEG signal classification. The findings suggest that the combination of graph attention and self-attention mechanisms is a promising approach for improving the accuracy and reliability of EEG-based diagnostics, potentially improving the management of neurological disorders.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550037"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012151","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

Self-Supervised Image Segmentation Using Meta-Learning and Multi-Backbone Feature Fusion. 基于元学习和多主干特征融合的自监督图像分割。

International journal of neural systems Pub Date : 2025-05-01 Epub Date: 2025-02-03 DOI: 10.1142/S0129065725500121

Muhammad Shahroz Ajmal, Guohua Geng, Xiaofeng Wang, Mohsin Ashraf

引用次数: 0

Coherence-Based Graph Convolution Network to Assess Brain Reorganization in Spinal Cord Injury Patients. 基于相干的图卷积网络评估脊髓损伤患者的脑重组。

International journal of neural systems Pub Date : 2025-05-01 Epub Date: 2025-03-15 DOI: 10.1142/S0129065725500212

Jiancai Leng, Jiaqi Zhao, Yongjian Wu, Chengyan Lv, Zhixiao Lun, Yanzi Li, Chao Zhang, Bin Zhang, Yang Zhang, Fangzhou Xu, Changsong Yi, Tzyy-Ping Jung

{"title":"Coherence-Based Graph Convolution Network to Assess Brain Reorganization in Spinal Cord Injury Patients.","authors":"Jiancai Leng, Jiaqi Zhao, Yongjian Wu, Chengyan Lv, Zhixiao Lun, Yanzi Li, Chao Zhang, Bin Zhang, Yang Zhang, Fangzhou Xu, Changsong Yi, Tzyy-Ping Jung","doi":"10.1142/S0129065725500212","DOIUrl":"10.1142/S0129065725500212","url":null,"abstract":"Motor imagery (MI) engages a broad network of brain regions to imagine a specific action. Investigating the mechanism of brain network reorganization during MI after spinal cord injury (SCI) is crucial because it reflects overall brain activity. Using electroencephalogram (EEG) data from SCI patients, we conducted EEG-based coherence analysis to examine different brain network reorganizations across different frequency bands, from resting to MI. Furthermore, we introduced a consistency calculation-based residual graph convolution (C-ResGCN) classification algorithm. The results show that the [Formula: see text]- and [Formula: see text]-band connectivity weakens, and brain activity decreases during the MI task compared to the resting state. In contrast, the [Formula: see text]-band connectivity increases in motor regions while the default mode network activity declines during MI. Our C-ResGCN algorithm showed excellent performance, achieving a maximum classification accuracy of 96.25%, highlighting its reliability and stability. These findings suggest that brain reorganization in SCI patients reallocates relevant brain resources from the resting state to MI, and effective network reorganization correlates with improved MI performance. This study offers new insights into the mechanisms of MI and potential biomarkers for evaluating rehabilitation outcomes in patients with SCI.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550021"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639865","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

Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model. 通过网络级计算模型了解局灶性癫痫中尖峰和棘波的时空耦合。

International journal of neural systems Pub Date : 2025-05-01 Epub Date: 2025-03-15 DOI: 10.1142/S0129065725500182

Min Pan, Qiang Li, Jiangling Song, Bo Wang, Wenhua Wang, Rui Zhang

{"title":"Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model.","authors":"Min Pan, Qiang Li, Jiangling Song, Bo Wang, Wenhua Wang, Rui Zhang","doi":"10.1142/S0129065725500182","DOIUrl":"10.1142/S0129065725500182","url":null,"abstract":"The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes and spindles (STCSSs) are defined as a kind of pathological waves, and frequent emergence of them may cause the degradation of cognitive function for FE patients. However, the neural mechanisms underlying STCSSs are not well understood. To this end, this work first develops a neural mass network model for focal epilepsy (FE-NMNM) with multiple thalamocortical columns being its nodes and the long-range synaptic interactions of thalamocortical columns being its edges, where each thalamocortical column is extended on the basis of Costa model and then they are connected through excitatory synapses between pyramidal cells. Then, how the cortico-cortical connectivity affects the evolution of STCSSs across the network is especially discussed by simulations in two cases, where the inter-ictal state and the ictal state are considered separately. Simulation results demonstrate that: (1) the more STCSSs occur in a more extensive area when the cortico-cortical connectivity becomes stronger, and the significant increase of coupling discharges is attributed to the presence of abundant spikes; (2) when the connectivity is excessively strong, the cortical hyperexcitability will happen, thereby inducing massive spike discharges which may further inhibit the occurrence of spindles, and hence, resulting in the disappearance of STCSSs. The obtained results provide a mechanistic insight into STCSSs, and suggest that such coupling patterns could reflect widespread network dysfunction in FE, thereby potentially advancing therapeutic strategies for FE.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550018"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625844","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

Adaptive Dynamic Surface Control of Epileptor Model Based on Nonlinear Luenberger State Observer. 基于非线性Luenberger状态观测器的癫痫模型自适应动态面控制。

International journal of neural systems Pub Date : 2025-05-01 DOI: 10.1142/S0129065725500224

Mahdi Kamali Dolatabadi, Marzieh Kamali, Farzaneh Shayegh

{"title":"Adaptive Dynamic Surface Control of Epileptor Model Based on Nonlinear Luenberger State Observer.","authors":"Mahdi Kamali Dolatabadi, Marzieh Kamali, Farzaneh Shayegh","doi":"10.1142/S0129065725500224","DOIUrl":"10.1142/S0129065725500224","url":null,"abstract":"Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, which are sudden bursts of electrical activity in the brain. The Epileptor model is a computational model specifically created to replicate the complex dynamics of epileptic seizures. The parameters of the Epileptor model can be adjusted to simulate activities associated with some seizure classes seen in patients. Due to the closeness of this model to nonlinear systems with nonstrict feedback form and the existence of uncertainties in the model, an adaptive dynamic surface controller is chosen for control of the system. Considering that the states in the Epileptor model are not measurable and the only measurable output is the Local Field Potentials signal, a nonlinear Luenberger state observer is developed to estimate the system states. It is the first time that the Luenberger state observer is used for the Epileptor model. In this approach, Radial Basis Neural Networks are utilized to estimate the system's nonlinear dynamics. The stability of our proposed controller along with the observer is proved, and the performance is shown using simulation. Simulation results show that by using the suggested method, the output and states of the, system track their reference, value with an acceptable error.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550022"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766138","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

Scene Text Detection Based on Text Stroke Components. 基于文本笔画分量的场景文本检测。

International journal of neural systems Pub Date : 2025-05-01 DOI: 10.1142/S0129065725500200

Xinyue Hou, Pengsen Cheng, Hongyu Gao, Xin Li, Jiayong Liu

{"title":"Scene Text Detection Based on Text Stroke Components.","authors":"Xinyue Hou, Pengsen Cheng, Hongyu Gao, Xin Li, Jiayong Liu","doi":"10.1142/S0129065725500200","DOIUrl":"10.1142/S0129065725500200","url":null,"abstract":"The detection of scene text holds significant importance across a variety of application scenarios. However, previous methods were insufficient for detecting and recognizing text instances, such as variations in text size, chaotic background and diverse text orientations. To address these challenges, this paper proposes a novel methodology based on Text Stroke Components (TSC). The method leverages Harris corner detection to identify critical points of text strokes, such as endpoints, turning points, and curvatures. By analyzing the clustered regions of these points, the approach effectively localizes text characters. To enhance the detection process, a transparency parameter [Formula: see text] is introduced to control the fusion between original images and corner-detection images. This improves the localization of key stroke points, and reduces background noise interference. The proposed method is evaluated through extensive experiments, demonstrating superior performance compared to existing scene text detectors. Furthermore, the method is jointly trained with the ABINet recognition model across all stages. Comprehensive experiments conducted on 13 datasets reveal that this approach significantly outperforms SOTA methods. These results underscore the advantages of using text stroke components for key-point localization through the corner detection algorithm in scene text detection.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550020"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766139","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 Heterogeneous Attractor Model for Neural Dynamical Mechanism of Movement Preparation. 运动准备神经动力学机制的异质吸引子模型。

International journal of neural systems Pub Date : 2025-05-01 Epub Date: 2025-03-01 DOI: 10.1142/S0129065725500194

Lining Yin, Lanyun Cui, Ying Yu, Qingyun Wang

{"title":"A Heterogeneous Attractor Model for Neural Dynamical Mechanism of Movement Preparation.","authors":"Lining Yin, Lanyun Cui, Ying Yu, Qingyun Wang","doi":"10.1142/S0129065725500194","DOIUrl":"10.1142/S0129065725500194","url":null,"abstract":"Preparatory activity is crucial for voluntary motor control, reducing reaction time and enhancing precision. To understand the neurodynamic mechanisms behind this, we construct a dynamical model within the motor cortex, which comprises coupled heterogeneous attractors to simulate delayed reaching tasks. This model replicates the neural activity patterns observed in the macaque motor cortex, within distinct attractor spaces for preparatory and executive activities. It can capture the transition from preparation to execution through shifts in an orthogonal subspace combined with a thresholding mechanism. Results show that the preparation duration modulates behavioral accuracy, with optimal preparation intervals enhancing performance. External inputs primarily shape the preparatory activity, while synaptic connections dominate execution. Our analysis of the network's multi-stable dynamics reveals that external inputs reshape the stable points of the heterogeneous attractor modules both before and after preparation, while synaptic strength affects dynamical stability and input sensitivity, allowing rapid and precise actions. Additionally, sensitivity to external perturbations decreases as preparatory time increases, emphasizing the importance of external inputs during preparation. Overall, this study provides insights into the neurodynamic mechanisms underlying the transition from motor preparation to execution and underscores the significance of preparatory activity for accurate motor control.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550019"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766249","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

On the Computational Complexity of Spiking Neural Membrane Systems with Colored Spikes. 带彩色尖峰的尖峰神经膜系统的计算复杂度。

International journal of neural systems Pub Date : 2025-04-30 DOI: 10.1142/S0129065725500352

Antonio Grillo, Claudio Zandron

引用次数: 0