Cognitive Neurodynamics最新文献

{"title":"Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols.","authors":"Honggang Liu, Xuanyu Jin, Dongjun Liu, Wanzeng Kong, Jiajia Tang, Yong Peng","doi":"10.1007/s11571-024-10214-w","DOIUrl":"10.1007/s11571-024-10214-w","url":null,"abstract":"<p><p>The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"31"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045783","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":"Brain analysis to approach human muscles synergy using deep learning.","authors":"Elham Samadi, Fereidoun Nowshiravan Rahatabad, Ali Motie Nasrabadi, Nader Jafarnia Dabanlou","doi":"10.1007/s11571-025-10228-y","DOIUrl":"10.1007/s11571-025-10228-y","url":null,"abstract":"<p><p>Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"44"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143491021","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":"Respiratory modulation of beta corticomuscular coherence in isometric hand movements.","authors":"Zhibin Li, Jingyao Sun, Tianyu Jia, Linhong Ji, Chong Li","doi":"10.1007/s11571-025-10245-x","DOIUrl":"10.1007/s11571-025-10245-x","url":null,"abstract":"<p><p>Respiration is a fundamental physiological function in humans, often synchronized with movement to enhance performance and efficiency. Recent studies have underscored the modulatory effects of respiratory rhythms on brain oscillations and various behavioral responses, including sensorimotor processes. In light of this connection, our study aimed to investigate the influence of different respiratory patterns on beta corticomuscular coherence (CMC) during isometric hand flexion and extension. Utilizing electroencephalogram (EEG) and surface electromyography (sEMG), we examined three breathing conditions: normal breathing, deep inspiration, and deep expiration. Two experimental protocols were employed: the first experiment required participants to simultaneously breathe and exert force, while the other involved maintaining a constant force while varying breathing patterns. The results revealed that deep inspiration significantly enhanced beta CMC during respiration-synchronized tasks, whereas normal breathing resulted in higher CMC compared to deep respiration during sustained force exertion. In the second experiment, beta CMC was cyclically modulated by respiratory phase across all breathing conditions. The difference in the outcomes from the two protocols demonstrated a task-specific modulation of respiration on motor control. Overall, these findings indicate the complex dynamics of respiration-related effects on corticomuscular neural communication and provide valuable insights into the mechanisms underpinning the coupling between respiration and motor function.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10245-x.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"54"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699812","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":"Detection of cognitive load during computer-aided education using infrared sensors.","authors":"Subashis Karmakar, Tandra Pal, Chiranjib Koley","doi":"10.1007/s11571-025-10242-0","DOIUrl":"10.1007/s11571-025-10242-0","url":null,"abstract":"<p><p>Technology integration in modern education has transformed traditional teaching-learning methods, but maintaining student attentiveness during computer-aided activities remains challenging. Neuroimaging advancements provide valuable insights into cognitive processes. This study measures cognitive load during computer-aided education. We have collected functional near-infrared spectroscopy (fNIRS) brain signals while subjects perform mental tasks and rest. Three datasets have been considered to evaluate the performance of the proposed model. The first two datasets are open-access, and we prepare the third dataset by collecting fNIRS brain signals from 14 healthy subjects. Two feature extraction techniques are proposed: manual and automatic based on wavelet scattering transform (WST). A one dimensional convolutional neural network (1D CNN) is also proposed to automatically extract features through feature engineering and classification. For comparison, four machine learning classifiers, linear discriminant analysis (LDA), Naive Bayes (NB), k-nearest neighbors (KNN) and support vector machine (SVM), are also considered. Classification performance is evaluated using accuracy, precision, recall and F1-score across all datasets. Computational cost, i.e., the CPU time and memory utilization for extracting the features and testing the classifiers, is also evaluated. The results suggest that when considering four classifiers across three datasets and comparing among the manual and the WST-based feature extraction methods, the average performance of 1D CNN is superior in terms of classification accuracy (1.16 times higher), precision (1.10 times higher), recall (1.10 times higher) and F1-score (1.09 times higher). However, the CPU time and memory utilization for 1D CNN are significantly higher, 10.09 and 14.70 times, respectively. In comparison to four state-of-the-art deep learning models, the proposed 1D CNN also shows best classification accuracy (92.99%). The analysis of the results shows that identifying cognitive load, SVM with Gaussian kernel function on WST based methods, provides satisfactory classification performance with significantly less CPU time and memory utilization.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"58"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794880","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":"Complexity and non-predictability in neurodynamic: gender-specific EEG dynamics uncovered via hidden markov models.","authors":"Fatemeh Zareayan Jahromy","doi":"10.1007/s11571-025-10271-9","DOIUrl":"10.1007/s11571-025-10271-9","url":null,"abstract":"<p><p>One area of interest in neuroscience is the study of differences between male and female brains, encompassing structural, physiological, and neural activity, as well as their implications for behavioral traits and functional capabilities. In this study, we investigate the differences in the complexity of EEG signals between men and women and propose hidden Markov model (HMM) method for measuring complexity which significantly improves the accuracy of gender-based classification compared to conventional signal complexity measures. Using this method to measure complexity of signal, we enhanced the results by reaching to 86% decoding accuracy. Additionally, we demonstrated that the observed effect is particularly dominant in the parietal, frontal and central regions of the brain. Through signal filtering, we observed that differences in signal complexity between men and women are present across most of frequency bands with a high rate of enhancement. It is also noteworthy that the level of complexity in women's brain activity is higher than in men's. The results of HMM method showed higher classification accuracy across most frequency bands compared to conventional methods for measuring signal complexity and nonlinearity, such as entropy, Lyapunov and Hurst exponent. Importantly, the performance improvement rate was significantly higher than that of other conventional methods. Additionally, our finding of higher signal complexity in female was entirely consistent with previous studies. Overall, the results demonstrated that using a Hidden Markov Model can more effectively extract signal complexity, significantly enhancing the accuracy of EEG-based gender classification<i>.</i></p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"87"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274339","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-derived brain connectivity in theta/alpha frequency bands increases during reading of individual words.","authors":"Fatemeh Delavari, Zachary Ekves, Roeland Hancock, Gerry T M Altmann, Sabato Santaniello","doi":"10.1007/s11571-025-10280-8","DOIUrl":"10.1007/s11571-025-10280-8","url":null,"abstract":"<p><p><b>Objective:</b> Although extensive insights about the neural mechanisms of reading have been gained via magnetic and electrographic imaging, the temporal evolution of the brain network during sight reading remains unclear. We tested whether the temporal dynamics of the brain functional connectivity involved in sight reading can be tracked using high-density scalp EEG recordings. <b>Approach:</b> Twenty-eight healthy subjects were asked to read words in a rapid serial visual presentation task while recording scalp EEG, and phase locking value was used to estimate the functional connectivity between EEG channels in the theta, alpha, beta, and gamma frequency bands. The resultant networks were then tracked through time. <b>Main results:</b> The network's graph density gradually increases as the task unfolds, peaks 150-250-ms after the appearance of each word, and returns to resting-state values, while the shortest path length between non-adjacent functional areas decreases as the density increases, thus indicating that a progressive integration between regions can be detected at the scalp level. This pattern was independent of the word's type or position in the sentence, occurred in the theta/alpha band but not in beta/gamma band, and peaked earlier in the alpha band compared to the theta band (alpha: 184 ± 61.48-ms; theta: 237 ± 65.32-ms, <i>P</i>-value <i>P</i> < 0.01). Nodes in occipital and frontal regions had the highest eigenvector centrality throughout the word's presentation, and no significant lead-lag relationship between frontal/occipital regions and parietal/temporal regions was found, which indicates a consistent pattern in information flow. In the source space, this pattern was driven by a cluster of nodes linked to sensorimotor processing, memory, and semantic integration, with the most central regions being similar across subjects. <b>Significance:</b> These findings indicate that the brain network connectivity can be tracked via scalp EEG as reading unfolds, and EEG-retrieved networks follow highly repetitive patterns lateralized to frontal/occipital areas during reading.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10280-8.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"90"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301261","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":"Non-Gaussianity of neurotransmitters co-released from mammalian adrenal chromaffin cells.","authors":"Ziheng Xu, Jingxiao Huo, Yanmei Kang, Changhe Wang","doi":"10.1007/s11571-025-10273-7","DOIUrl":"10.1007/s11571-025-10273-7","url":null,"abstract":"<p><p>While synaptic currents in computational neuroscience are conventionally modeled as Gaussian processes, there tends to be theoretical assumption that non-Gaussian Lévy processes can better describe the stochastic nature of neurotransmitter release in real neurophysiological scenarios. To support this view, we conduct statistical inference with the recordings of the co-release currents of two neurotransmitters from mammalian adrenal chromaffin cells by two steps. First, both the deterministic part and the random part of the current time series are separated by local weighted regression based on the individual vesicle releases and the entire co-release process, respectively. By fitting the resultant deterministic parts in individual release by the double exponential function and the counterparts in the entire co-release process by the truncated Fourier series, the procedure of separation we adopt is validated. And then, the statistical analysis based on the quantile-quantile plot and the empirical characteristic function reveals that the distribution of the random parts dramatically deviates from Gaussian distribution but matches well with certain non-Gaussian alpha stable distribution. Thus, the present study provides significant evidence for the non-Gaussian nature about neurotransmitter release from biophysical experiment.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"92"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332589","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 neural computational and dynamical mechanisms of reward-modulated spatial coding in hippocampal place cells.","authors":"Qi Shao, Yihong Wang, Xuying Xu, Yaning Wang, Xiaochuan Pan, Ying Du, Rubin Wang","doi":"10.1007/s11571-025-10282-6","DOIUrl":"10.1007/s11571-025-10282-6","url":null,"abstract":"<p><p>Hippocampal place cells play a critical role in mammalian spatial navigation, episodic memory formation, and other relevant spatial cognitive functions. Experimental evidences suggest that when animals perform spatial navigation tasks in real or virtual environments, the number of place fields in the region adjacent to the target or reward location is significantly higher than in distal regions, a place cell representation phenomenon defined as \"over-representation\". The \"over-representation\" phenomenon shows dynamic changes in spatial representation: when the reward or target location moves, the location of maximum place field density shifts to the new reward position - a process termed \"over-representation shift\". Despite significant progress in understanding over-representation, current explanations predominantly focus on qualitative descriptions, lacking a comprehensive computational framework to systematically elucidate underlying neural mechanisms of over-representation. To address this question, we developed two distinct but related place cell sub-models based on the continuous attractor network framework: the Position-Integrated Model, which dynamically encodes spatial locations through place cell activity, and the Velocity-Driven Model, which incorporates speed cells to encode animal's movement speed. Both sub-models successfully achieved the path integration function observed in rodents. Building upon these foundational models, we implemented a reward-location-dependent dynamic gain mechanism to simulate goal-directed navigation in one-dimensional (1D) linear tracks and two-dimensional (2D) square environments. This mechanism dynamically modulates neural activity gains according to the Euclidean distance between reward locations and the animal's position. Our simulations revealed that place cells exhibit over-representation within 5-10 cm of reward zones, and the spatial distribution of place fields dynamically tracking reward location changes. This framework successfully reproduces over-representation and the dynamic shift of over-representation in place cells, revealing how reward locations shape spatial representations and trigger place field reorganization. These findings enhance our comprehension of hippocampal mechanisms in reward-based spatial navigation and establish a computational basis for studying experience-dependent neural remapping.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"99"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12185856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495031","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":"A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.","authors":"Dengpei Ji, Haiqing Yu, Xiaolin Xiao, Yongzhi Huang, Xiaoyu Zhou, Minpeng Xu, Tzyy-Ping Jung, Dong Ming","doi":"10.1007/s11571-025-10279-1","DOIUrl":"10.1007/s11571-025-10279-1","url":null,"abstract":"<p><p>Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"101"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526659","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":"Passivity and dissipativity-based fuzzy control of quaternion-valued memristive neural networks on time scales.","authors":"Ruoxia Li, Jinde Cao, Zhengwen Tu","doi":"10.1007/s11571-025-10300-7","DOIUrl":"10.1007/s11571-025-10300-7","url":null,"abstract":"<p><p>In this paper, the problem of passivity and dissipativity analysis are investigated for a class of fractional-order quaternion-valued fuzzy memristive neural networks. By constructing proper Lyapunov functional and employing inequality technique, several improved passivity criteria and dissipativity conclusions are established, which can be checked efficiently by use of some standard mathematical calculations. Different from previous results, involving the quaternions connections, our derivation avoid considering the \"magnitude\" of quaternion. Finally, two simulation examples based on the fuzzy model are given to demonstrate the effectiveness of the proposed techniques.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"109"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552514","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}