Cognitive Neurodynamics最新文献_第9页

Dynamic analysis of multi-spiral chaotic inertia model with a cyclic configuration involving four homogeneous HNN cells: stability analysis, analog and digital verifications. 包含四个均匀HNN单元的循环构型多螺旋混沌惯性模型的动力学分析：稳定性分析、模拟和数字验证。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-04-18 DOI: 10.1007/s11571-025-10240-2

Jean Baptiste Koinfo, Donghua Jiang, Jean Chamberlain Chedjou, Jacques Kengne, Khabibullo Nosirov

{"title":"Dynamic analysis of multi-spiral chaotic inertia model with a cyclic configuration involving four homogeneous HNN cells: stability analysis, analog and digital verifications.","authors":"Jean Baptiste Koinfo, Donghua Jiang, Jean Chamberlain Chedjou, Jacques Kengne, Khabibullo Nosirov","doi":"10.1007/s11571-025-10240-2","DOIUrl":"10.1007/s11571-025-10240-2","url":null,"abstract":"This paper investigates the behavior of a Hopfield neural network consisting of four interconnected inertial neurons arranged in a loop configuration. The mathematical equation that governs the overall dynamic of the model is consists of a set of eight first-order ordinary differential equations (ODEs) with odd symmetry. The system has 81 equilibrium points, some of which undergo multiple Hopf bifurcations as a control parameter is varied. The maximum number of coexisting states is related to the maximum number of active equilibrium points. Through numerical investigations, intriguing nonlinear properties are discovered, including both homogeneous and heterogeneous multistability and the coexistence of up to sixteen bifurcation branches, the presence of multi-spiral chaos, crisis phenomenon, period splitting and the oscillation death phenomenon. In order to obtain a comprehensive understanding of the dynamics, various tools are used, such as phase portraits, bifurcation diagrams, Poincare maps, frequency spectra, Lyapunov exponent spectra, and attraction basins. A Significant achievement of this study is the demonstration that coupling inertial neurons can be an effective method to generate multi-spiral chaotic signals. The overall dynamics is non-hidden and meticulous adjustment of the gradient connected to the fourth neuron allows to complete annihilate oscillations (no motion) in the neural network in a particular interval. Finally, an electronic circuit inspired by the coupled inertial neuron system is designed using Orcad-PSpice software and implemented using an Arduino-based microcontroller. The simulation results from PSpice and microcontroller confirm the findings from the theoretical analysis.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"63"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981892","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

ECn-MultiBSTM: multiclass epileptic seizure classification using electro cetacean optimized bidirectional long short-term memory model. ECn-MultiBSTM：基于电鲸类优化的双向长短期记忆模型的多类别癫痫发作分类。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-27 DOI: 10.1007/s11571-025-10268-4

Pankaj Kunekar, Pankaj Dadheech, Mukesh Kumar Gupta

{"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":"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.","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}

引用次数: 0

Neurofusionnet: a comprehensive framework for accurate epileptic seizure prediction from EEG data with hybrid meta-heuristic optimization algorithm. Neurofusionnet：一个综合框架，准确预测癫痫发作从脑电图数据与混合元启发式优化算法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-07-12 DOI: 10.1007/s11571-025-10293-3

Tejashwini P S, Sahana L, Thriveni J, Venugopal K R

{"title":"Neurofusionnet: a comprehensive framework for accurate epileptic seizure prediction from EEG data with hybrid meta-heuristic optimization algorithm.","authors":"Tejashwini P S, Sahana L, Thriveni J, Venugopal K R","doi":"10.1007/s11571-025-10293-3","DOIUrl":"https://doi.org/10.1007/s11571-025-10293-3","url":null,"abstract":"This work uses cutting edge Electroencephalogram (EEG) data processing techniques to present a complete paradigm for epileptic seizure prediction. The methodology is a multi-step procedure that includes pre-processing, feature extraction, feature selection, and a new detection model based on deep learning for enhanced durability and accuracy. Bandpass filtering is used to reduce noise during the pre-processing phase, which improves the signal-to-noise ratio. EEG data quality is further improved using Independent Component Analysis, which finds and removes artifacts. Splitting continuous EEG data into fixed-duration segments, known as epoching, facilitates the investigation of discrete temporal patterns. Standard amplitude values are guaranteed by Z-score normalization, and seizure-related patterns are more sensitively detected when channels are selected using Common Spatial Patterns. Step one of the feature extraction processes involves statistical features and time-domain features. For spectrum information it is essential to recognizing seizures, frequency-domain features such as Power spectrum Density are extracted using a technique Fourier Transform. A full representation is obtained by extracting Time-Frequency Domain Features with the Wavelet Transform. Predictive power is increased by the efficient selection of discriminative characteristics through the use of a hybrid optimization model called Hybrid Chimp Enhanced Fox Optimization algorithm that combines optimization methods inspired by FOX and Chimp. The suggested NeuroFusionNet-based detection model combines Improved ShuffleNet V2, SqueezeNet, EfficientNet V2, and Multi Head Attention (MHA) based GhostNet V2, which captures complex patterns linked to epileptic episodes.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"113"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636384","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 hybrid deep learning architectures and feature extraction techniques for alzheimer disease recognition. 一种用于阿尔茨海默病识别的混合深度学习架构和特征提取技术。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-09-11 DOI: 10.1007/s11571-025-10330-1

A Nivethitha, T Manigandan

{"title":"A hybrid deep learning architectures and feature extraction techniques for alzheimer disease recognition.","authors":"A Nivethitha, T Manigandan","doi":"10.1007/s11571-025-10330-1","DOIUrl":"10.1007/s11571-025-10330-1","url":null,"abstract":"Alzheimer's disease (AD) is one of the common forms of dementia and is tremendously increasing throughout the world. There are many biomarkers currently available to detect the AD progression. In AD, brain cell death occurs, leading to memory loss, impaired calculation ability, and difficulty in remembering recent events. Early detection of AD is crucial for managing the symptoms and providing effective medical intervention. AD symptoms usually develop gradually and become worse over time, and interfere with daily activities. Hence, this research proposes the Fuzzy scoring based ResNet-Convolutional Neural Network (FS-ResNet CNN) to discriminate AD patients having AD, Mild Cognitive Impairment (MCI), and cognitively normal (CN) using a hybrid deep learning architecture to leverage more complete spatial information from the ADNI data. Initially, the pre-processing is carried out using the z-score normalization. To reduce the time complexity and to select the prominent features, the Adaptive Grey Wolf Optimization Algorithm (AGWOA), harnessing the swarm intelligence, has been proposed. Finally, the Hybrid Deep Learning Architecture is applied for the classification of AD. Specifically, the proposed method introduces a novel method known as the Fuzzy Scoring to optimize the network performance. Furthermore, the proposed FS-ResNet CNN model is computationally efficient, less sensitive to noise, and efficiently saves memory. Experimental results demonstrate the effectiveness of the proposed method on the ADNI dataset, showing high classification accuracy of 97.89%, surpassing the other state-of-the-art methods.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"146"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063536","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

Multi-level cognitive state classification of learners using complex brain networks and interpretable machine learning. 使用复杂脑网络和可解释机器学习的学习者多层次认知状态分类。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-03 DOI: 10.1007/s11571-024-10203-z

Xiuling He, Yue Li, Xiong Xiao, Yingting Li, Jing Fang, Ruijie Zhou

{"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":"10.1007/s11571-024-10203-z","url":null,"abstract":"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.","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}

引用次数: 0

Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks. 无线传感器网络中自适应信号处理的认知神经动力学方法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10190-1

K G Shanthi, A Mary Joy Kinol, S Rukmani Devi, K Kannan

{"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":"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.","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}

引用次数: 0

Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study. 经皮耳迷走神经刺激（taVNS）对运动规划的影响：一项多模态信号研究。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-24 DOI: 10.1007/s11571-025-10220-6

Long Chen, Yihao Hu, Zhongpeng Wang, Lei Zhang, Chuxiang Jian, Shengcui Cheng, Dong Ming

{"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":"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.","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}

引用次数: 0

BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition. BISNN：用于增强基于脑电图的情感识别的生物信息融合尖峰神经网络。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-03-22 DOI: 10.1007/s11571-025-10239-9

Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo

{"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":"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.","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}

引用次数: 0

Shap-driven explainable AI with simulated annealing for optimized seizure detection using multichannel EEG signal. 形状驱动的可解释人工智能与模拟退火优化癫痫检测使用多通道脑电图信号。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-05 DOI: 10.1007/s11571-025-10269-3

Indu Dokare, Sudha Gupta

{"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":"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.","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}

引用次数: 0

Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns. 经颅磁声刺激对兴奋性和抑制性神经元放电模式的影响。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1007/s11571-025-10290-6

Yiming Li, Haoyu Qiu, Haijun Zhu

{"title":"Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns.","authors":"Yiming Li, Haoyu Qiu, Haijun Zhu","doi":"10.1007/s11571-025-10290-6","DOIUrl":"10.1007/s11571-025-10290-6","url":null,"abstract":"Transcranial Magneto-Acoustical Stimulation (TMAS) represents an innovative, highly efficacious, and non-invasive modality for brain stimulation. Neurons, as integral components of neural networks, are crucial for the transmission of information. Nevertheless, the impact of TMAS on the discharge patterns of both excitatory and inhibitory neurons is not yet fully understood. To address this gap, the Hodgkin-Huxley neuronal model is analyzed using the improved Euler method. The neuronal discharge patterns are comprehensively examined by systematically adjusting ion channel parameters and stimulation parameters. The results indicate that ultrasound frequency exerted minimal influence on the properties of neuronal action potentials. Conversely, as the static magnetic field strength and ultrasound power are augmented, the excitability of both types of neurons progressively enhances. However, the changes in the electrical properties of action potentials are less pronounced in inhibitory neurons compared to excitatory neurons. Furthermore, alterations in ion channel parameters significantly influence the firing characteristics of both types of neurons. The present study elucidates that TMAS has a significant effect on the firing patterns of excitatory and inhibitory neurons. Excitatory neurons showed stronger regular discharges in response to static magnetic fields and increased ultrasound power, whereas inhibitory neurons did not respond to low-intensity static magnetic fields. In addition, our systematic analysis revealed synergistic effects between ion channel parameters and TMAS stimulation parameters. These findings shed light on how neuron type specificity and ion channel dynamics work together to shape the efficacy of TMAS, thus advancing previous studies.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"108"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552512","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