Cognitive Neurodynamics最新文献_第5页

Dynamics study of double-column model and its application in epilepsy EEG. 双柱模型的动态研究及其在癫痫脑电中的应用。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1007/s11571-025-10334-x

Yuhua Xu, Ying Du, Xuying Xu, Yihong Wang

{"title":"Dynamics study of double-column model and its application in epilepsy EEG.","authors":"Yuhua Xu, Ying Du, Xuying Xu, Yihong Wang","doi":"10.1007/s11571-025-10334-x","DOIUrl":"https://doi.org/10.1007/s11571-025-10334-x","url":null,"abstract":"The human brain constitutes a highly complex nonlinear network, comprising billions of interconnected neurons capable of rapid and precise responses to diverse internal and external perturbations. Disruptions in neural connectivity or functional impairments within this network can lead to neurological disorders, including epilepsy. In this study, we propose an improved double-column neural model, derived from the Jansen-Rit (JR) framework, to investigate the effects of external stimuli on epileptiform electroencephalogram (EEG) across multiple cortical regions. Our model specifically targets the signal transmission delays and dynamic synaptic interactions within and between cortical columns. Simulations demonstrate that the improved double-column model successfully reproduces diverse EEG phenomena, including alpha rhythms and epileptiform discharges, across distinct cortical layers. When configured within the same cortical region, the model exhibits symmetry dynamics governed by two connection constants, which is predictable within the symmetry framework of the system, validating its plausibility. Notably, in inter-cortical double-column simulations, parametric modulation of coupling strengths generated varied prefrontal cortical epileptiform discharge patterns. Most significantly, applying targeted external stimuli to visual cortex columns induced a state transition in prefrontal cortex column activity, shifting from epileptic like discharges to stable alpha rhythm, which did not occur in the single-column experiment. These findings suggest that focal neuromodulation of specific cortical regions could serve as a potential therapeutic strategy for suppressing pathological activity in epilepsy.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"148"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085216","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}

引用次数: 0

Astrocytic signatures in neuronal activity: a machine learning-based identification approach. 神经元活动中的星形细胞特征：一种基于机器学习的识别方法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-11 DOI: 10.1007/s11571-025-10276-4

João Pedro Pirola, Paige DeForest, Paulo R Protachevicz, Laura Fontenas, Ricardo F Ferreira, Rodrigo F O Pena

{"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":"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.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10276-4.","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}

引用次数: 0

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images. 基于卷积自编码器的深度学习脑CT图像脑出血分类。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI: 10.1007/s11571-025-10259-5

B Nageswara Rao, U Rajendra Acharya, Ru-San Tan, Pratyusa Dash, Manoranjan Mohapatra, Sukanta Sabut

{"title":"Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.","authors":"B Nageswara Rao, U Rajendra Acharya, Ru-San Tan, Pratyusa Dash, Manoranjan Mohapatra, Sukanta Sabut","doi":"10.1007/s11571-025-10259-5","DOIUrl":"10.1007/s11571-025-10259-5","url":null,"abstract":"Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 (\"ICH\" class) and 1648 (\"Normal\" class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical Sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"77"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119113","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}

引用次数: 0

A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection. 一种新的基于Goldner-Harary图的量子启发模式，用于阿尔茨海默病的自动检测。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-10 DOI: 10.1007/s11571-025-10249-7

Ilknur Sercek, Niranjana Sampathila, Irem Tasci, Tuba Ekmekyapar, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U R Acharya

{"title":"A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.","authors":"Ilknur Sercek, Niranjana Sampathila, Irem Tasci, Tuba Ekmekyapar, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U R Acharya","doi":"10.1007/s11571-025-10249-7","DOIUrl":"https://doi.org/10.1007/s11571-025-10249-7","url":null,"abstract":"Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"71"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981499","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}

引用次数: 0

Neural dynamics of deception: insights from fMRI studies of brain states. 欺骗的神经动力学：来自大脑状态的功能磁共振成像研究的见解。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-02-20 DOI: 10.1007/s11571-025-10222-4

Weixiong Jiang, Lin Li, Yulong Xia, Sajid Farooq, Gang Li, Shuaiqi Li, Jinhua Xu, Sailing He, Xiangyu Wu, Shoujun Huang, Jing Yuan, Dexing Kong

{"title":"Neural dynamics of deception: insights from fMRI studies of brain states.","authors":"Weixiong Jiang, Lin Li, Yulong Xia, Sajid Farooq, Gang Li, Shuaiqi Li, Jinhua Xu, Sailing He, Xiangyu Wu, Shoujun Huang, Jing Yuan, Dexing Kong","doi":"10.1007/s11571-025-10222-4","DOIUrl":"10.1007/s11571-025-10222-4","url":null,"abstract":"Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed truth-preferred and lie-preferred-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10222-4.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"42"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482401","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}

引用次数: 0

SH-StNN: prognostication of Alzheimer's disease based on search and hunt-based stacked deep convolutional neural network. SH-StNN：基于搜索和狩猎的堆叠深度卷积神经网络对阿尔茨海默病的预测。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1007/s11571-025-10329-8

Umakant Mandawkar, Tausif Diwan

{"title":"SH-StNN: prognostication of Alzheimer's disease based on search and hunt-based stacked deep convolutional neural network.","authors":"Umakant Mandawkar, Tausif Diwan","doi":"10.1007/s11571-025-10329-8","DOIUrl":"https://doi.org/10.1007/s11571-025-10329-8","url":null,"abstract":"The conventional Machine Learning (ML) approaches for Alzheimer's disease (AD) detection using MRI images deployed the complex feature extraction strategies, consumed huge training time, and exhibited poor detection results. Particularly, Convolutional Neural Networks (CNNs) failed to capture long-range correlations from different brain regions, and suffer from overfitting issues. Hence, Select and Hunt Optimized Stacked Deep Convolutional Neural Network (SH-StNN) is proposed that automatically captures the intricate patterns associated with the brain structures, resulting in accurate detection for the effective AD detection. Architecturally, SH-StNN is constructed with the stacked-CNN layers, where RELU activation function is used. In this research, the Select and Hunt Optimization (SHO) algorithm is applied for medical image segmentation and effective classifier training, which optimizes the fifteenth layer of SH-StNN model. The experimental analysis demonstrates that the SH-StNN model shows improved accuracy of 98%, outperforming the existing techniques, such as Deep CNN by 13.17%, and CT-GAN by 10.81% for 80% of the training using the ADNI dataset. Additionally, the proposed SH-StNN model reports the accuracy of 96.73%, sensitivity of 96.90%, and specificity of 96.96% for the OASIS dataset.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"167"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12528530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145328423","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}

引用次数: 0

The role of beta band phase resetting in audio-visual temporal order judgment. 波段相位重置在视听时间顺序判断中的作用。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-15 DOI: 10.1007/s11571-024-10183-0

Yueying Li, Yasuki Noguchi

{"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":"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.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10183-0.","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}

引用次数: 0

DWT-OEFS: discrete wavelet transform based optimized ensemble feature selection for Parkinson's disease severity classification. DWT-OEFS：基于离散小波变换的帕金森病严重程度分类优化集成特征选择。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-08-08 DOI: 10.1007/s11571-025-10312-3

Sneha Agrawal, Satya Prakash Sahu

{"title":"DWT-OEFS: discrete wavelet transform based optimized ensemble feature selection for Parkinson's disease severity classification.","authors":"Sneha Agrawal, Satya Prakash Sahu","doi":"10.1007/s11571-025-10312-3","DOIUrl":"https://doi.org/10.1007/s11571-025-10312-3","url":null,"abstract":"Parkinson's disease (PD) is a cognitive degenerative condition of central nervous system which highly impacts the motor function, resulting in gait dysfunction. Determining the severity of PD is essential for timely and efficient medical management. Doctors often utilize clinical manifestations to grade the severity of PD using Hoehn & Yahr scale where their evaluation is heavily reliant on skill and experience. We propose an optimized ensemble metaheuristic-based feature selection framework by utilizing the signal processing techniques to grade the severity of PD on publicly available Physionet gait Vertical Ground Reaction Force dataset obtained using wearable device. Due to scarcity of medical dataset, the sample size is increased by segmentation of signal. Discrete wavelet transform (DWT) decomposes the signal and a total of 13 features including statistical, frequency and entropy-base are extracted. For an optimum subset of features, three bio-inspired metaheuristic algorithms Binary Grey Wolf Optimization, Binary Whale Optimization and Binary Dragonfly algorithm are used for optimized ensemble feature selection (OEFS) to prevent dimensionality curse thereby improving the classification accuracy. Further, the class imbalance issue is addressed via SMOTETomek and the selected features are then subjected to four best performing classifiers and weighted voting-based classifier. The suggested model is assessed using variety of performance assessment techniques like accuracy, precision, recall, F1-score and Mathew's Correlation Coefficient. The ensemble model achieves the maximum classification accuracy of 98.56% for multiclass classification through weighted voting. Our proposed approach outperforms existing models and individual classifiers, demonstrating its ability to accurately forecast and classify PD severity.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"126"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12334388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815999","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}

引用次数: 0

Memristor-based RDBO-CNN circuit design and application of image multi-classification recognition. 基于忆阻器的rbo - cnn图像多分类识别电路设计及应用。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1007/s11571-025-10323-0

Gaoyong Han, Guanxiang Cheng, Yanfeng Wang, Junwei Sun

{"title":"Memristor-based RDBO-CNN circuit design and application of image multi-classification recognition.","authors":"Gaoyong Han, Guanxiang Cheng, Yanfeng Wang, Junwei Sun","doi":"10.1007/s11571-025-10323-0","DOIUrl":"10.1007/s11571-025-10323-0","url":null,"abstract":"Traditional convolutional neural networks used for classification largely rely on hyperparameter tuning and do not have the conditions for hardware implementation. Therefore, a memristor crossbar architecture circuit is proposed to implement the reinforced dung beetle optimization (RDBO) algorithm and the convolutional neural network (CNN). The circuit is composed of feeding module, storage module, ball rolling module, dance module, subpopulation module and CNN module. Traditional DBO algorithm with its adaptability and parallelism for CNN parameter optimization, there are some shortcomings. To solve the problem of unbalanced exploration and exploitation, the tendency to fall into local optimal state, an enhanced dung beetle optimization algorithm based on giant dung beetle and spiral search is proposed. The RDBO circuit is composed of feeding module, storage module, ball rolling module, dance module and subpopulation module. The CNN module is composed of convolution layer, pooling layer and fully connected layer, which is used to recognize and classify the image. The feasibility and accuracy of RDBO-CNN circuit are verified on MNIST image set. In order to further verify the effectiveness of the proposed circuit, simulation and comparison experiments are carried out the satellite image recognition RSI-CB image set which also has good accuracy. This will further promote the development and application of neural network technology.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"128"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945489","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}

引用次数: 0

Motor imagery EEG classification method using 3D CNN and LSTM for rehabilitation application. 基于3D CNN和LSTM的运动图像脑电分类方法在康复中的应用。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-08-19 DOI: 10.1007/s11571-025-10317-y

Yuejiang Hao, Shiwei Cheng

{"title":"Motor imagery EEG classification method using 3D CNN and LSTM for rehabilitation application.","authors":"Yuejiang Hao, Shiwei Cheng","doi":"10.1007/s11571-025-10317-y","DOIUrl":"10.1007/s11571-025-10317-y","url":null,"abstract":"Due to the limitations in the accuracy and robustness of current EEG classification methods, applying motor imagery for practical Brain-Computer Interface applications remains challenging. Therefore, an EEG classification method with high accuracy and strong robustness is of significant importance. This paper proposed a method called 3D CNN and LSTM for Motor Imagery (3D-CLMI), which combines 3D CNN and LSTM network with attention to classify MI-EEG signals. This method combined MI-EEG signals from different channels into 3D features and extracted spatial features through convolution operations with multiple 3D convolutional kernels of different scales. At the same time, in order to ensure the integrity of the extracted temporal features of the MI-EEG signal, 3D-CLMI adopted a parallel structure to obtain spatial and temporal features respectively, and then combined the obtained features for classification. Experimental results showed that this method achieved a classification accuracy of 92.7% and an F1-score of 0.91 on BCI Competition IV 2a, which were both higher than the state-of-the-art methods in the field of MI tasks. Additionally, 12 participants were invited to complete a four-class MI task, and experiments on the collected dataset showed that our method also maintained the highest classification accuracy and F1-score. Our proposed method achieved the best results on both datasets, and we then demonstrated the effectiveness of each part of the proposed method through ablation experiments. Additionally, we designed a rehabilitation application system in a VR environment based on the proposed method, and the experimental results validated that it could assist patients with impaired hand motor function.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"131"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945523","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}

引用次数: 0