Cognitive Neurodynamics最新文献

{"title":"EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.","authors":"Yutao Miao, Kaijie Li, Wenhao Zhao, Yushi Zhang","doi":"10.1007/s11571-025-10278-2","DOIUrl":"10.1007/s11571-025-10278-2","url":null,"abstract":"<p><p>Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"94"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging low-frequency components for enhanced high-frequency steady-state visual evoked potential based brain computer interface in fast calibration scenario.","authors":"Yixin Chen, Ren Xu, Andrew Ty Lau, Xinjie He, Weijie Chen, Xingyu Wang, Andrzej Cichocki, Jing Jin","doi":"10.1007/s11571-025-10303-4","DOIUrl":"https://doi.org/10.1007/s11571-025-10303-4","url":null,"abstract":"<p><p>High-frequency steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems offer improved user comfort but suffer from reduced performance compared to their low-frequency counterparts, limiting their practical application. To address this issue, we propose a transfer learning-based method that leverages low-frequency SSVEP data to enhance high-frequency SSVEP performance. A filtering mechanism is designed to extract informative components from low-frequency signals, and the least squares algorithm is employed to generate high-quality synthetic high-frequency data. Experiments conducted on two public datasets using TDCA, eTRCA, and advanced TRCA-based algorithms demonstrate significant performance improvements. Our approach requires only two calibration trials, achieving 9.03% and 14.49% accuracy increases for eTRCA and TDCA in Dataset 1, and 13.91% and 14.53% improvements in Dataset 2, all within 1.5 s. Moreover, our approach effectively addresses the issue of single calibration data for high-frequency SSVEP-BCI systems. These results support the feasibility of fast calibration and improved performance in real-world high-frequency BCI applications.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"124"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144783585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurodynamic evidence reveals identity top-down influences emotional contagion of race.","authors":"Chao Kong, Yanqiu Wei, Ping Hu","doi":"10.1007/s11571-025-10322-1","DOIUrl":"10.1007/s11571-025-10322-1","url":null,"abstract":"<p><p>Faces contain important information about emotion, race, identity, and age. A large body of research has illustrated that emotional contagion is influenced by race. The Categorization-Individuation Model (CIM) suggests that situational cues (e.g., authority, subjectively important ingroup-outgroup) cause perceivers to shift their attention to identity-diagnostic facial characteristics, especially for other-race faces. The current study is designed to reveal whether identity can top-down influence emotional contagion across races, and the time course of this influence. We recruited 30 Chinese college students to participate in two experiments. Experiment 1 used dynamic emotional faces of Asians and Whites to assess emotional contagion in different races. Experiment 2, based on experiment 1, employed a minimal group paradigm assigning identity information to the racial faces. We used ERP analysis to predict the potential neural mechanism of the influence of identity on racial emotion contagion, and used representation similarity analysis (RSA) to explore the temporal dynamics of the representation of race, emotion, and identity. Our results showed that (1) in experiment 1, Whites produced stronger P1 amplitudes than Asians; in experiment 2, RSA results showed that the time course of representation of race was about 100 ms. (2) In experiments 1 and 2, Happy produced stronger P200 amplitude than Angry; Asians produced stronger P200 amplitude than Whites; The RSA results showed that the time course of representation of emotion and emotional contagion both began about 200 ms after face appearance. (3) In experiment 2, the P300 amplitudes showed a significant interaction of identity and race, and in different group conditions, the P300 amplitude in Asians was stronger than in Whites; however, in the same group conditions, the difference between the two races was insignificant. Results illustrate that identity information top-down influences the neural mechanisms of racial emotional contagion, and the effects are divided into at least three stages: (1) an early stage bottom-up perceptual categorization of other-race; (2) a middle stage emotional and individualization processing; and (3) a late stage top-down modulation by identity cues. Our study is the first to explain the neurodynamics of emotional contagion processing using the Categorization-Individuation Model.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"134"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing mental disorders with a CNN-GRU deep learning model on motor activity.","authors":"Umang Gupta, Partha Sarathi Bishnu, Abhishek Kumar, Anuj Kumar Pandey, Biresh Kumar, Preeti Kumari","doi":"10.1007/s11571-025-10335-w","DOIUrl":"https://doi.org/10.1007/s11571-025-10335-w","url":null,"abstract":"<p><p>Mood disorders can significantly interfere with daily life, ranging from mild to severe, impacting relationships, work, and overall well-being. Globally, the scarcity of mental health resources and the stigma attached to mental illness are significant obstacles. Existing approaches for mood disorder detection often rely on static clinical data or other modalities (e.g., imaging or questionnaires), and the potential of continuous motor activity data remains underexplored. Continuous wearable motor activity recordings represent an objective, non-invasive method that tracks an individual's behavioral patterns relevant to their mood states, while enabling ongoing monitoring in contrast to the episodic clinical assessments. Our primary goal in this paper is to employ a Deep Learning Model utilizing CNN-GRU architecture for analyzing motor activity sequences. Through rigorous experimentation on Depresjon datasets recorded via wrist worn actigraphy, our approach achieves an accuracy of 98.1%, surpassing the accuracy levels achieved by state-of-the-art techniques.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"147"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-repair mechanisms of spiking neuron-astrocyte networks in working memory under diverse injury conditions.","authors":"Bingyi Mo, Xiaoqian Liu, Lin Li, Shanshan Cheng, Yuan Zhu, Ming Yi, Lulu Lu","doi":"10.1007/s11571-025-10345-8","DOIUrl":"https://doi.org/10.1007/s11571-025-10345-8","url":null,"abstract":"<p><p>The injury to neurons and connection structures in the nervous system is a key factor leading to neurodegenerative diseases. Self-repair function refers to the innate capacity of the neuron-astrocyte network to partially restore or maintain its function following injury, without external intervention. When the brain's nervous system is injured, how self-repair mechanisms work under various injury conditions and how to improve self-repair function remain unresolved. Through computational simulations of three distinct neurological injury scenarios, we investigated the self-repair function of spiking neuron-astrocyte networks in working memory tasks. Despite varying degrees of disruption of the network, all experiments (Self-Repair activated by synaptic connection injury, astrocytes injury, and internal noise interference) reveal that astrocytes can promote self-repair of the network during working memory tasks. Experiments on synaptic connection injury demonstrated that the network can maintain effective repair functionality under high injury conditions, which is associated with elevated calcium ion concentrations induced by increased glutamate release from presynaptic neurons. The modulation of astrocyte contributes to self-repair, and self-repair function decreases with increasing astrocyte injury. In addition, compared to the health network, internal noise interference has a small enhancement in the self-repair function of the network. Our findings elucidate the critical role of astrocyte-mediated signaling in maintaining network under different synaptic injury. This provides novel mechanistic insights into the threshold dynamics governing neuron network stability and early pathological transition in response to diverse neural injuries.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"160"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Differential impact of repetitive transcranial magnetic stimulation on alzheimer's disease symptomology: evidence from electrovestibulography.","authors":"Zeinab A Dastgheib, Brian J Lithgow, Zahra K Moussavi","doi":"10.1007/s11571-025-10355-6","DOIUrl":"https://doi.org/10.1007/s11571-025-10355-6","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-025-10310-5.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"164"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Impact of Repetitive Transcranial Magnetic Stimulation on Alzheimer's Disease Symptomology: Evidence from Electrovestibulography Does repetitive transcranial magnetic stimulation treatment alter Alzheimer's disease symptomology? a clue to show who will benefit.","authors":"Zeinab A Dastgheib, Brian J Lithgow, Zahra K Moussavi","doi":"10.1007/s11571-025-10310-5","DOIUrl":"10.1007/s11571-025-10310-5","url":null,"abstract":"<p><strong>Background: </strong>Repetitive transcranial magnetic stimulation (rTMS) has shown promise in enhancing cognitive function through neuroplasticity. This study investigates the impact of rTMS on Alzheimer's disease (AD) and AD with cerebrovascular disease (AD-CVD) symptomologies, using Electrovestibulography (EVestG).</p><p><strong>Methodology: </strong>Participants were recruited from a randomized, double-blind, placebo-controlled clinical trial on rTMS efficacy for mild to moderate AD. Thirty-five individuals who volunteered for the EVestG study (28 received active rTMS and 7 the sham treatment) were recorded at baseline, post-treatment, and two months' follow-up. EVestG recordings were analyzed to calculate normalized probability (NP) values for AD and AD-CVD symptomologies and compare with standard cognitive outcome.</p><p><strong>Results: </strong>Changes in NP values from pre to post active treatment showed improved participants exhibited opposite trends in AD and AD-CVD symptomologies compared to non-improved participants with a decrease in NP_AD-CVD and a slight increase in NP_AD value. Significant associations were found between changes in cognitive score and NP values, even after adjusting for age, sex, and multiple comparisons, indicating that patients with higher certainty of AD diagnosis (versus AD-CVD) were more likely to benefit from rTMS.</p><p><strong>Conclusion: </strong>These findings suggest rTMS cognitive improvement may result from reduced AD-CVD symptomatology, especially in patients with higher certainty of AD diagnoses, potentially due to increased cerebral blood flow (CBF).</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10310-5.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"120"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12311084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144774829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal transition of resting-state brain networks associates with human cognitive abilities.","authors":"Lv Zhou, Zhengchang Jiang, Zhao Chang, Rong Wang, Ying Wu","doi":"10.1007/s11571-025-10347-6","DOIUrl":"https://doi.org/10.1007/s11571-025-10347-6","url":null,"abstract":"<p><p>The brain is a dynamic system that continuously switches between different states. This brain state transition has significant functional consequences on human cognition, but its dynamic mechanism is rarely understood. Here, we quantified the state transition by measuring the spatiotemporal reconfiguration of modular structure spanning time and space in the resting-brain functional networks. By integrating multimodal data, noise-driven large-scale dynamic model and meta-analysis, we found the significant relationship between state transition and brain evolution indicated by human accelerated regions (HARs) genes. This state transition was associated with diverse cognitive abilities, especially better executive control ability in the default mode network and control network. The resting-state brain showed a moderate degree of state transition at the whole-brain scale, but the regional heterogeneity of the transition was the highest, which functionally, was associated with the dynamic balance between segregation and integration, and structurally, was supported by hierarchical modules in brain structural connectivity. In addition, the high state transition among regions was supported by serotonin 1 A (5-HT_1A) and dopamine (D₂) receptors. Our findings highlight the critical role of brain state transition in cognitive abilities and reveal the underlying dynamic mechanisms, offering new insights into the functional principles of the resting brain.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10347-6.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"163"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12496318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaching CORnet human fMRI representations for enhanced model-brain alignment.","authors":"Zitong Lu, Yile Wang","doi":"10.1007/s11571-025-10252-y","DOIUrl":"https://doi.org/10.1007/s11571-025-10252-y","url":null,"abstract":"<p><p>Deep convolutional neural networks (DCNNs) have demonstrated excellent performance in object recognition and have been found to share some similarities with brain visual processing. However, the substantial gap between DCNNs and human visual perception still exists. Functional magnetic resonance imaging (fMRI) as a widely used technique in cognitive neuroscience can record neural activation in the human visual cortex during the process of visual perception. Can we teach DCNNs human fMRI signals to achieve a more brain-like model? To answer this question, this study proposed ReAlnet-fMRI, a model based on the SOTA vision model CORnet but optimized using human fMRI data through a multi-layer encoding-based alignment framework. This framework has been shown to effectively enable the model to learn human brain representations. The fMRI-optimized ReAlnet-fMRI exhibited higher similarity to the human brain than both CORnet and the control model in within- and across-subject as well as within- and across-modality model-brain (fMRI and EEG) alignment evaluations. Additionally, we conducted an in-depth analysis to investigate how the internal representations of ReAlnet-fMRI differ from CORnet in encoding various object dimensions. These findings provide the possibility of enhancing the brain-likeness of visual models by integrating human neural data, helping to bridge the gap between computer vision and visual neuroscience.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10252-y.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"61"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mtfsfn: a multi-view time-frequency-space fusion network for EEG-based emotion recognition.","authors":"Zhongmin Wang, Shengyang Gao","doi":"10.1007/s11571-025-10342-x","DOIUrl":"https://doi.org/10.1007/s11571-025-10342-x","url":null,"abstract":"<p><p>Over the recent years, emotion recognition based on electroencephalogram (EEG) has emerged as a prominent research area. Nevertheless, EEG signals present spatially discrete and non-stationary characteristics, to represent spatiotemporal information and extract more discriminative features from complex signals is still a challenge. This study proposed a multi-view time-frequency-space fusion network, referred to as MTFSFN. To effectively utilize complementary information from different frequency bands, we employ a frequency-domain attention mechanism to allocate weights to features of different frequency bands. A multi-view Transformer model was designed, integrating Transformer with two-dimensional positional embeddings to extract discrete spatial information. Following the fusion of multi-view features, we utilize LSTM to capture dynamic time-frequency-space relationships. Finally, a subject-independent leave-one-subject-out cross-validation strategy was used to validate extensively on three public datasets, DEAP, SEED, and SEED-IV. On the DEAP dataset, the average accuracies of valence and arousal are 78.64% and 77.42%, respectively. On the SEED dataset, the average accuracy is 86.91%. On the SEED-IV dataset, the average accuracy is 75.51%. The experimental results show that the proposed MTFSFN model achieves excellent recognition performance.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"155"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}