工程技术最新文献

{"title":"TCANet: a temporal convolutional attention network for motor imagery EEG decoding.","authors":"Wei Zhao, Haodong Lu, Baocan Zhang, Xinwang Zheng, Wenfeng Wang, Haifeng Zhou","doi":"10.1007/s11571-025-10275-5","DOIUrl":"10.1007/s11571-025-10275-5","url":null,"abstract":"<p><p>Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"91"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309661","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":"Astrocytic signatures in neuronal activity: a machine learning-based identification approach.","authors":"João Pedro Pirola, Paige DeForest, Paulo R Protachevicz, Laura Fontenas, Ricardo F Ferreira, Rodrigo F O Pena","doi":"10.1007/s11571-025-10276-4","DOIUrl":"10.1007/s11571-025-10276-4","url":null,"abstract":"<p><p>This study investigates the expanding role of astrocytes, the predominant glial cells, in brain function, focusing on whether and how their presence influences neuronal network activity. We focus on particular network activities identified as synchronous and asynchronous. Using computational modeling to generate synthetic data, we examine these network states and find that astrocytes significantly affect synaptic communication, mainly in synchronous states. We use different methods of extracting data from a network and compare which is best for identifying glial cells, with mean firing rate emerging with higher accuracy. To reach the aforementioned conclusions, we applied various machine learning techniques, including Decision Trees, Random Forests, Bagging, Gradient Boosting, and Feedforward Neural Networks, the latter outperforming other models. Our findings reveal that glial cells play a crucial role in modulating synaptic activity, especially in synchronous networks, highlighting potential avenues for their detection with machine learning models through experimental accessible measures.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10276-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"89"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301260","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":"Visual statistical learning based on a coupled shape-position recurrent neural network model.","authors":"Baolong Sun, Yihong Wang, Xuying Xu, Xiaochuan Pan","doi":"10.1007/s11571-025-10285-3","DOIUrl":"10.1007/s11571-025-10285-3","url":null,"abstract":"<p><p>The visual system has the ability to learn the statistical regularities (temporal and/or spatial) that characterize the visual scene automatically and implicitly. This ability is referred to as the visual statistical learning (VSL). The VSL could group several objects that have fixed statistical properties into a chunk. This complex process relies on the collaborative involvement of multiple brain regions that work together to learn the chunk. Although behavioral experiments have explored cognitive functions of the VSL, its computational mechanisms remain poorly understood. To address this issue, this study proposes a coupled shape-position recurrent neural network model based on the anatomical structure of the visual system to explain how chunk information is learned and represented in neural networks. The model comprises three core modules: the position network, which encodes object position information; the shape network, which encodes object shape information; and the decision network, which integrates the neuronal activity in the position and shape networks to make decisions. The model successfully simulates the results of a classic spatial VSL experiment. The distribution of neural firing rates in the decision network shows a significant difference between chunk and non-chunk conditions. Specifically, these neurons in the chunk condition exhibit stronger firing rates than those in the non-chunk condition. Furthermore, after the model learns a scene containing both chunk and non-chunk stimuli, neurons in the position network selectively encode far and near stimuli, respectively. In contrast, neurons in the shape network distinguish between chunk and non-chunk. The chunk encoding neurons selectively respond to specific chunks. These results indicate that the proposed model is able to learn spatial regularities of the stimuli to discriminate chunks from non-chunks, and neurons in the shape network selectively respond to chuck and non-chunk information. These findings offer important theoretical insights into the representation mechanisms of chunk information in neural networks and propose a new framework for modeling spatial VSL.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"96"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332590","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":"Emotion recognition framework based on adaptive window selection and CA-KAN.","authors":"Xuefen Lin, Linhui Fan, Yifan Gu, Zhixian Wu","doi":"10.1007/s11571-025-10283-5","DOIUrl":"10.1007/s11571-025-10283-5","url":null,"abstract":"<p><p>In recent years, emotion recognition, particularly EEG-based emotion recognition, has found widespread application across various domains. Enhancing EEG data processing and emotion recognition models remains a key research focus in this field. This paper presents an emotion recognition framework combining the CUSUM algorithm-based adaptive window selection technique with the convolutional attention-enhanced Kolmogorov-Arnold Networks (CA-KAN). The improved CUSUM algorithm effectively extracts the most emotion-relevant segments from raw EEG data. Furthermore, by enhancing the KAN network, the CA-KAN model achieves both high accuracy and efficiency in emotion recognition. The proposed framework achieved peak classification accuracies of 94.63% and 94.73% on the SEED and SEED-IV datasets, respectively. Additionally, the framework offers a lightweight advantage, demonstrating significant potential for real-world applications, including medical emotion monitoring and driver emotion detection.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"100"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12187633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144504983","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 role of beta band phase resetting in audio-visual temporal order judgment.","authors":"Yueying Li, Yasuki Noguchi","doi":"10.1007/s11571-024-10183-0","DOIUrl":"10.1007/s11571-024-10183-0","url":null,"abstract":"<p><p>The integration of auditory and visual stimuli is essential for effective language processing and social perception. The present study aimed to elucidate the mechanisms underlying audio-visual (A-V) integration by investigating the temporal dynamics of multisensory regions in the human brain. Specifically, we evaluated inter-trial coherence (ITC), a neural index indicative of phase resetting, through scalp electroencephalography (EEG) while participants performed a temporal-order judgment task that involved auditory (beep, A) and visual (flash, V) stimuli. The results indicated that ITC phase resetting was greater for bimodal (A + V) stimuli compared to unimodal (A or V) stimuli in the posterior temporal region, which resembled the responses of A-V multisensory neurons reported in animal studies. Furthermore, the ITC got lager as the stimulus-onset asynchrony (SOA) between beep and flash approached 0 ms. This enhancement in ITC was most clearly seen in the beta band (13-30 Hz). Overall, these findings highlight the importance of beta rhythm activity in the posterior temporal cortex for the detection of synchronous audiovisual stimuli, as assessed through temporal order judgment tasks.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10183-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"28"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001022","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":"<i>ZmNF-YB10</i>, a maize NF-Y transcription factor, positively regulates drought and salt stress response in <i>Arabidopsis thaliana</i>.","authors":"Yimeng Wang, Peng Jiao, Chenyang Wu, Chunlai Wang, Ke Shi, Xiaoqi Gao, Shuyan Guan, Yiyong Ma","doi":"10.1080/21645698.2024.2438421","DOIUrl":"https://doi.org/10.1080/21645698.2024.2438421","url":null,"abstract":"<p><p>Maize (<i>Zea mays</i> L.) is a major food and feed crop and an important raw material for energy, chemicals, and livestock. The NF-Y family of transcription factors in maize plays a crucial role in the regulation of plant development and response to environmental stress. In this study, we successfully cloned and characterized the maize NF-Y transcription factor gene <i>ZmNF-YB10</i>. We used bioinformatics, quantitative fluorescence PCR, and other techniques to analyze the basic properties of the gene, its tissue expression specificity, and its role in response to drought, salt, and other stresses. The results indicated that the gene was 1209 base pairs (bp) in length, with a coding sequence (CDS) region of 618 bp, encoding a polypeptide composed of 205 amino acid residues. This polypeptide has a theoretical isoelectric point of 5.85 and features a conserved structural domain unique to the NF-Y family. Quantitative fluorescence PCR results demonstrated that the <i>ZmNF-YB10</i> gene was differentially upregulated under drought and salt stress treatments but exhibited a negatively regulated expression pattern under alkali and cold stress treatments. Transgenic <i>Arabidopsis thaliana</i> subjected to drought and salt stress in soil showed greener leaves than wild-type <i>A. thaliana</i>. In addition, the overexpression lines showed reduced levels of hydrogen peroxide (H₂O₂), superoxide (O^2-), and malondialdehyde (MDA) and increased activities of peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD). Western blot analysis revealed a distinct band at 21.8 kDa. Salt and drought tolerance analyses conducted in <i>E. coli</i> BL21 indicated a positive regulation. In yeast cells, <i>ZmNF-YB10</i> exhibited a biological function that enhances salt and drought tolerance. Protein interactions were observed among the <i>ZmNF-YB10</i>, <i>ZmNF-YC2</i>, and <i>ZmNF-YC4</i> genes. It is hypothesized that the <i>ZmNF-YB10, ZmNF-YC2</i>, and <i>ZmNF-YC4</i> genes may play a role in the response to abiotic stresses, such as drought and salt tolerance, in maize.</p>","PeriodicalId":54282,"journal":{"name":"Gm Crops & Food-Biotechnology in Agriculture and the Food Chain","volume":"16 1","pages":"28-45"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural oscillations predict flow experience.","authors":"Bingxin Lin, Baoshun Guo, Lingyun Zhuang, Dan Zhang, Fei Wang","doi":"10.1007/s11571-024-10205-x","DOIUrl":"10.1007/s11571-024-10205-x","url":null,"abstract":"<p><p>Flow experience, characterized by immersion in the activity at hand, provides a motivational boost and promotes positive behaviors. However, the oscillatory representations of flow experience are still poorly understood. In this study, the difficulty of the video game was adjusted to manipulate the individual's personalized flow or non-flow state, and EEG data was recorded throughout. Our results show that, compared to non-flow tasks, flow tasks exhibit higher theta power, moderate alpha power, and lower beta power, providing evidence for a focused yet effortless brain pattern during flow. Additionally, we employed Lasso regression to predict individual subjective flow scores based on neural data, achieving a correlation coefficient of 0.571 (<i>p</i> < 0.01) between the EEG-predicted scores and the actual self-reported scores. Our findings offer new insights into the oscillatory representation of flow and emphasize that flow, as a measure of individual experience quality, can be objectively and quantitatively predicted through neural oscillations.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"1"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920787","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":"Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging.","authors":"Shuang Wang, Bo Li, Minghe Xu, Chunlian Chen, Zhe Liu, Yuqing Ji, Shaowen Qian, Kai Liu, Gang Sun","doi":"10.1007/s11571-024-10206-w","DOIUrl":"https://doi.org/10.1007/s11571-024-10206-w","url":null,"abstract":"<p><p>Insomnia is a common mental illness seriously affecting people lives, that might progress to major depression. However, the neural mechanism of patients with CID comorbid MDD remain unclear. Combining fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC), this study investigated abnormality in local and long-range neural activity of patients with CID comorbid MDD. Here, we acquired resting-state blood oxygenation level dependent (BOLD) data from 57 patients with CID comorbid MDD and 57 healthy controls (HC). Compared with the controls, patients with CID comorbid MDD exhibited abnormal functional activity in posterior cerebral cortex related to the visual cortex, including the middle occipital gyrus (MOG), the cuneus and the lingual gyrus, specifically, lower fALFF values in the right MOG, left cuneus, and right postcentral gyrus, increased FC between the right MOG and the left cerebellum, and decreased FC between the right MOG and the right lingual gyrus. Neuropsychological correlation analysis revealed that the decreased fALFF in the right MOG was negatively correlated with all the neuropsychological scores of insomnia and depression, reflecting common relationships with symptoms of CID and MDD. While the decreased fALFF of the left cuneus was distinctly correlated with the scores of depression related scales. The decreased FC between the right MOG and the right lingual gyrus was distinctly correlated with the scores of insomnia related scales. This study not only widened neuroimaging evidence that associated with insomnia and depressive symptoms of patients with CID comorbid MDD, but also provided new potential targets for clinical treatment.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"8"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945837","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":"Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction.","authors":"Ming Ke, Xinyi Yao, Peihui Cao, Guangyao Liu","doi":"10.1007/s11571-024-10191-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10191-0","url":null,"abstract":"<p><p>Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10191-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"7"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945880","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":"Unraveling the functional complexity of the locus coeruleus-norepinephrine system: insights from molecular anatomy to neurodynamic modeling.","authors":"Chun-Wang Su, Fan Yang, Runchen Lai, Yanhai Li, Hadia Naeem, Nan Yao, Si-Ping Zhang, Haiqing Zhang, Youjun Li, Zi-Gang Huang","doi":"10.1007/s11571-024-10208-8","DOIUrl":"10.1007/s11571-024-10208-8","url":null,"abstract":"<p><p>The locus coeruleus (LC), as the primary source of norepinephrine (NE) in the brain, is central to modulating cognitive and behavioral processes. This review synthesizes recent findings to provide a comprehensive understanding of the LC-NE system, highlighting its molecular diversity, neurophysiological properties, and role in various brain functions. We discuss the heterogeneity of LC neurons, their differential responses to sensory stimuli, and the impact of NE on cognitive processes such as attention and memory. Furthermore, we explore the system's involvement in stress responses and pain modulation, as well as its developmental changes and susceptibility to stressors. By integrating molecular, electrophysiological, and theoretical modeling approaches, we shed light on the LC-NE system's complex role in the brain's adaptability and its potential relevance to neurological and psychiatric disorders.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"29"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045808","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}