Cognitive Neurodynamics最新文献

{"title":"A novel predefined-time projective synchronization strategy for multi-modal memristive neural networks.","authors":"Hui Zhao, Lei Zhou, Aidi Liu, Sijie Niu, Xizhan Gao, Xiju Zong, Xin Li, Lixiang Li","doi":"10.1007/s11571-025-10234-0","DOIUrl":"10.1007/s11571-025-10234-0","url":null,"abstract":"<p><p>Due to its complexity, the problem of predefined-time synchronization in multimodal memristive neural networks has rarely been explored in the literature. This paper is the first to systematically study this issue, filling a research gap in the field and further enriching the related theoretical framework. First, a novel predefined-time stability theorem is proposed, which features more lenient judgment conditions compared to existing methods. This significantly enhances the generality of the stability theorem, making it applicable to a wider range of practical engineering projects. Second, based on the proposed predefined-time stability theorem, as well as the theories of differential inclusion, Filippov solutions, and set-valued mapping, a simple and practical feedback controller is developed. This controller establishes the necessary criteria for achieving predefined-time projective synchronization in multimodal memristive neural networks. Finally, two intricate simulation experiments are carefully designed. These experiments validate the effectiveness and feasibility of the theoretical derivations presented in this paper.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"50"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11910476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647570","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":"Focal attention peaks and laterality bias in problem gamblers: an eye-tracking investigation.","authors":"Yayoi Shigemune, Akira Midorikawa","doi":"10.1007/s11571-025-10238-w","DOIUrl":"10.1007/s11571-025-10238-w","url":null,"abstract":"<p><p>Problem gambling has been associated with attentional biases toward gambling-related stimuli, but less is known about how problem gamblers distribute their visual attention during gambling tasks. This eye-tracking study investigated differences in sustained visual attention between problem gamblers (PGs; <i>n</i> = 22) and non-problem gamblers (NPGs; <i>n</i> = 22) during a gambling task using neutral picture pairs. While total gaze time toward stimuli did not differ between the groups, PGs showed distinctive characteristics in their visual attentional allocation. Specifically, two-sample <i>t</i>-tests revealed that PGs exhibited significantly higher focal attention to right-sided stimuli in central zones (0-25 pixels) during decision-making, while NPGs demonstrated greater left-sided peripheral attention (76-100 pixels) during feedback. These patterns were further supported by a three-way ANOVA showing a significant group × zone × laterality interaction in the decision phase, confirming that PGs exhibited significantly higher right-sided attention in the central zone (0-25 and 26-50 pixels), while NPGs showed a tendency toward greater left-sided attention in the peripheral zone (76-100 pixels). Additionally, PGs demonstrated stronger rightward attentional bias in both phases. These differences in visual attention were associated with higher behavioral-approach-system, reward sensitivity, and sensation-seeking scores among PGs. The findings suggest that PGs exhibit distinctive characteristics in terms of sustained visual attention during gambling-related decision-making, even when viewing neutral stimuli. This distinctive distribution of visual attention may reflect fundamental differences in information processing and potential hemispheric imbalances in attention control mechanisms among PGs.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"51"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699809","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":"Shared neural dynamics of facial expression processing.","authors":"Madeline Molly Ely, Géza Gergely Ambrus","doi":"10.1007/s11571-025-10230-4","DOIUrl":"10.1007/s11571-025-10230-4","url":null,"abstract":"<p><p>The ability to recognize and interpret facial expressions is fundamental to human social cognition, enabling navigation of complex interpersonal interactions and understanding of others' emotional states. The extent to which neural patterns associated with facial expression processing are shared between observers remains unexplored, and no study has yet examined the neural dynamics specific to different emotional expressions. Additionally, the neural processing dynamics of facial attributes such as sex and identity in relation to facial expressions have not been thoroughly investigated. In this study, we investigated the shared neural dynamics of emotional face processing using an explicit facial emotion recognition task, where participants made two-alternative forced choice (2AFC) decisions on the displayed emotion. Our data-driven approach employed cross-participant multivariate classification and representational dissimilarity analysis on EEG data. The results demonstrate that EEG signals can effectively decode the sex, emotional expression, and identity of face stimuli across different stimuli and participants, indicating shared neural codes for facial expression processing. Multivariate classification analyses revealed that sex is decoded first, followed by identity, and then emotion. Emotional expressions (angry, happy, sad) were decoded earlier when contrasted with neutral expressions. While identity and sex information were modulated by image-level stimulus features, the effects of emotion were independent of visual image properties. Importantly, our findings suggest enhanced processing of face identity and sex for emotional expressions, particularly for angry faces and, to a lesser extent, happy faces.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10230-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"45"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566282","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":"Reciprocal causation relationship between rumination thinking and sleep quality: a resting-state fMRI study.","authors":"Shiyan Yang, Xu Lei","doi":"10.1007/s11571-025-10223-3","DOIUrl":"10.1007/s11571-025-10223-3","url":null,"abstract":"<p><p>Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: <i>n</i> = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10223-3.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"41"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482404","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":"Cross-subject mental workload recognition using bi-classifier domain adversarial learning.","authors":"Yueying Zhou, Pengpai Wang, Peiliang Gong, Peng Wan, Xuyun Wen, Daoqiang Zhang","doi":"10.1007/s11571-024-10215-9","DOIUrl":"10.1007/s11571-024-10215-9","url":null,"abstract":"<p><p>To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"16"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969952","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":"Monitoring nap deprivation-induced fatigue using fNIRS and deep learning.","authors":"Pei Ma, Chenyang Pan, Huijuan Shen, Wushuang Shen, Hui Chen, Xuedian Zhang, Shuyu Xu, Jingzhou Xu, Tong Su","doi":"10.1007/s11571-025-10219-z","DOIUrl":"10.1007/s11571-025-10219-z","url":null,"abstract":"<p><p>Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation. fNIRS signals in ten channels at the prefrontal cortex were collected, changes in blood oxygen concentration were analyzed, followed by a deep learning model to classify fatigue states. For the high-dimensionality and multi-channel characteristics of the fNIRS signal data, a novel 1D revised CNN-ResNet network was proposed based on the double-layer channel attenuation residual block. The results showed a 97.78% accuracy in fatigue state classification, significantly superior than several conventional methods. Furthermore, a fatigue-arousal experiment was designed to explore the feasibility of forced arousal of fatigued subjects through exercise stimulation. The fNIRS results showed a significant increase in brain activity with the conduction of exercise. The proposed method serves as a reliable tool for the evaluation of fatigue states, potentially reducing fatigue-induced harms and risks.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"30"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045786","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":"EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks.","authors":"Zahra Raeisi, Omid Bashiri, MohammadReza EskandariNasab, Mahdi Arshadi, Alireza Golkarieh, Hossein Najafzadeh","doi":"10.1007/s11571-025-10251-z","DOIUrl":"https://doi.org/10.1007/s11571-025-10251-z","url":null,"abstract":"<p><p>Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"68"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995738","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":"Conditional probabilistic-based domain adaptation for cross-subject EEG-based emotion recognition.","authors":"Shichao Cheng, Yifan Wang, Jiawei Mei, Guang Lin, Jianhai Zhang, Wanzeng Kong","doi":"10.1007/s11571-025-10272-8","DOIUrl":"10.1007/s11571-025-10272-8","url":null,"abstract":"<p><p>Electroencephalogram (EEG)-based emotion recognition has received increasing attention in affective computing. Due to the non-stationary and non-linear characteristics of EEG signals, EEG data exhibit significant individual differences. Previous studies have adopted domain adaptation strategies to minimize the distribution gap between individuals and achieved reasonable results. However, due to ignoring the influence of individual-dependent background signals on task-dependent emotional signals, most of the research can only align source domain data and target domain data spatially as a whole. There may be confusion between categories. Based on this limitation, this paper proposes a conditional probabilistic-based domain adversarial network (CPDAN) for cross-subject EEG-based emotion recognition. According to the characteristics of cross-subject EEG signals, CPDAN uses different branch networks to separate the background features and task features from EEG signals. In addition, CPDAN uses domain-adversarial training to model the discrepancy in the global domain and local domain to reduce the intra-class distance and enlarge the inter-class distance. The extensive experiments on SEED and SEED-IV demonstrate that our proposed CPDAN framework outperforms the comparison methods. Especially on SEED-IV, the average accuracy of CPDAN has improved by 22% over the comparison method.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"84"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233400","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":"Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.","authors":"Wenlong Ding, Aiping Liu, Xingui Chen, Chengjuan Xie, Kai Wang, Xun Chen","doi":"10.1007/s11571-025-10264-8","DOIUrl":"10.1007/s11571-025-10264-8","url":null,"abstract":"<p><p>The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"81"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173271","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":"The length and the width of the human brain circuit connections are strongly correlated.","authors":"Dániel Hegedűs, Vince Grolmusz","doi":"10.1007/s11571-024-10201-1","DOIUrl":"10.1007/s11571-024-10201-1","url":null,"abstract":"<p><p>The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber count, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at https://braingraph.org, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber count. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"21"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969960","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}