Cognitive Neurodynamics最新文献

{"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":"ECn-MultiBSTM: multiclass epileptic seizure classification using electro cetacean optimized bidirectional long short-term memory model.","authors":"Pankaj Kunekar, Pankaj Dadheech, Mukesh Kumar Gupta","doi":"10.1007/s11571-025-10268-4","DOIUrl":"10.1007/s11571-025-10268-4","url":null,"abstract":"<p><p>Multiclass epileptic seizureclassification aims to identify and categorize different epileptic seizure types like a non-epileptic seizure, epileptic interictal seizure, and epileptic ictal seizurein individuals based on Electroencephalography (EEG) signal characteristics. Multi-class seizure classification requires recognizing various seizure forms and patterns, which can be challenging due to noise and high variability patterns in EEG signals. Existing models face limitations such as difficulty in handling the complex and dynamic nature of seizure patterns, poor generalization to unseen data, and sensitivity to noise and artifacts, all of which impact classification accuracy and reliability. To address these issues, the Electro Cetacean Optimization based Multi Bidirectional Long Short-Term Memory (ECn-MultiBSTM) model is proposed. The BiLSTM modelis utilized for feature extraction, which captures sequential data by processing data in both forward and backward directions. This bidirectional approach enables the model to identify subtle patterns that distinguish various seizure types with higher accuracy. The ECn-MultiBSTM model also incorporates advanced Electro Cetacean optimizationtechniques that enhance its ability to search efficiently for optimal solutions.Through dynamic social coordination and rapid search strategies, the model fine-tunes its hyperparameters, ensuring improved performance and adaptability.The proposed ECn-MultiBSTM model significantly enhances multiclassseizure classification performance, achieving impressive metrics of 95.84% accuracy, 95.30% precision, 95.54% F1-score,0.94% MCC, 95.79% sensitivity, and 95.88% specificity when evaluated on the CHB-MIT SCALP EEG dataset.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"83"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180545","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":"BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition.","authors":"Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo","doi":"10.1007/s11571-025-10239-9","DOIUrl":"10.1007/s11571-025-10239-9","url":null,"abstract":"<p><p>Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model's effectiveness and evolution, aligning well with previous findings.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"52"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699844","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":"Multi-level cognitive state classification of learners using complex brain networks and interpretable machine learning.","authors":"Xiuling He, Yue Li, Xiong Xiao, Yingting Li, Jing Fang, Ruijie Zhou","doi":"10.1007/s11571-024-10203-z","DOIUrl":"https://doi.org/10.1007/s11571-024-10203-z","url":null,"abstract":"<p><p>Identifying the cognitive state can help educators understand the evolving thought processes of learners, and it is important in promoting the development of higher-order thinking skills (HOTS). Cognitive neuroscience research identifies cognitive states by designing experimental tasks and recording electroencephalography (EEG) signals during task performance. However, most of the previous studies primarily concentrated on extracting features from individual channels in single-type tasks, ignoring the interconnection across channels. In this study, three learning activities (i.e., video watching activity, keyword extracting activity, and essay creating activity) were designed based on a revised Bloom's taxonomy and the Interactive-Constructive-Active-Passive framework and used with 31 college students. The EEG signals were recorded when they were engaged in these activities. First, whole-brain network temporal dynamics were characterized by EEG microstate sequence analysis. Such dynamic changes rely on learning activity and corresponding functional brain systems. Subsequently, phase locking value was used to construct synchrony-based functional brain networks. The network characteristics were extracted to be inputted into different machine learning classifiers: Support Vector Machine, K-Nearest Neighbour, Random Forest, and eXtreme Gradient Boosting (XGBoost). XGBoost showed superior performance in the classification of cognitive states, with an accuracy of 88.07%. Furthermore, SHapley Additive exPlanations (SHAP) was adopted to reveal the connections between different brain regions that contributed to the classification of cognitive state. SHAP analysis reveals that the connections in the frontal, temporal, and central regions are most important for the high cognitive state. Collectively, this study may provide further evidence for educators to design cognitive-guided instructional activities to enhance learners' HOTS.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"5"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930821","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":"Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks.","authors":"K G Shanthi, A Mary Joy Kinol, S Rukmani Devi, K Kannan","doi":"10.1007/s11571-024-10190-1","DOIUrl":"10.1007/s11571-024-10190-1","url":null,"abstract":"<p><p>In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework. By leveraging Modified Distributed Arithmetic (MDA) which optimizes arithmetic operations by replacing the multipliers with lookup tables (LUT) hence minimizing energy consumption as well as computational complexity. Offset Binary Coding (OBC) enhanced the efficiency of data transmission by minimizing the data representation overhead. In addition to this, the adaptive strategy is incorporated with the Adaptive Finite Impulse Response (AFIR) framework permitting the filters to dynamically adjust to varying signal characteristics, thus offering high noise suppression and low distortion rates. Comprehensive simulations and comparative analysis validate the effectiveness of the proposed MDA-OBC-based AFIR method. The proposed method attained a lower energy consumption of 1.5 J and 130 W power consumption than the traditional implementations, resulting in significant energy efficiency and data transmission in signal preprocessing and noise suppression in WSNs.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"11"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969905","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":"Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.","authors":"Long Chen, Yihao Hu, Zhongpeng Wang, Lei Zhang, Chuxiang Jian, Shengcui Cheng, Dong Ming","doi":"10.1007/s11571-025-10220-6","DOIUrl":"10.1007/s11571-025-10220-6","url":null,"abstract":"<p><p>Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"35"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045764","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":"Shap-driven explainable AI with simulated annealing for optimized seizure detection using multichannel EEG signal.","authors":"Indu Dokare, Sudha Gupta","doi":"10.1007/s11571-025-10269-3","DOIUrl":"10.1007/s11571-025-10269-3","url":null,"abstract":"<p><p>The aim of this research is to combine Explainable AI (XAI) with advanced optimization techniques to provide a unique framework for seizure detection. This proposed work investigates how to enhance patient-specific and patient-non-specific seizure detection models by combining multiband feature extraction, SHAP-based feature selection, SMOTE, and a metaheuristic algorithm for hyperparameter tuning.The discrete wavelet transform (DWT) is used to decompose EEG signals to retrieve entropy-based and statistical information. Simulated Annealing (SA) is employed to optimize the Random Forest (RF) classifier's hyperparameters, and SHAP (SHapley Additive exPlanations) values are utilized for feature selection. Furthermore, a novel technique SHAP-RELFR has been demonstrated to select patient-non-specific features. Additionally, SMOTE is employed to handle imbalanced data. The proposed methodology is evaluated on the CHB-MIT and Siena datasets using both patient-specific and patient-non-specific feature selection approaches. Experimental findings demonstrate that the proposed methodology significantly improves the performance of seizure detection. The average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 96.58%, 95.19%, 94.52%, 98.02%, 94.72%, and 0.9452, respectively, using the CHB-MIT dataset. For the Seina dataset, the average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 94.81%, 94.51%, 94.04%, 96.87%, 94.28%, and 0.9400, respectively. Explainable AI combined with SMOTE and a metaheuristic optimization algorithm facilitates an enhanced seizure detection. The novel SHAP-RELFR method provides an effective patient-non-specific feature selection, enabling this approach to be applicable across diverse patients. This proposed framework offers a step toward enhancing clinical decision-making by providing interpretable and versatile seizure detection models.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"85"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246797","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}