Cognitive Neurodynamics最新文献_第6页

Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity. 大脑中距离依赖的连接促进了高度动态和结构的复杂性。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-12-26 DOI: 10.1007/s11571-025-10398-9

Victor J Barranca

{"title":"Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.","authors":"Victor J Barranca","doi":"10.1007/s11571-025-10398-9","DOIUrl":"10.1007/s11571-025-10398-9","url":null,"abstract":"Recent experiments have revealed that the inter-regional connectivity of the cerebral cortex exhibits strengths spanning over several orders of magnitude and decaying with distance. We demonstrate this to be a fundamental organizing feature that fosters high complexity in both connectivity structure and network dynamics, achieving an advantageous balance between integration and differentiation of information. This is verified through analysis of a multi-scale neuronal network model with nonlinear integrate-and-fire dynamics, incorporating inter-regional connection strengths decaying exponentially with spatial separation at the macroscale as well as small-world local connectivity at the microscale. Through numerical simulation and optimization over the model parameterspace, we show that inter-regional connectivity over intermediate spatial scales naturally facilitates maximally heterogeneous connection strengths, agreeing well with experimental measurements. In addition, we formulate complementary notions of structural and dynamical complexity, which are computationally feasible to calculate for large multi-scale networks, and we show that high complexity manifests for each over a similar parameter regime. We expect this work may help explain the link between distance-dependence in brain connectivity and the richness of neuronal network dynamics in achieving robust brain computations and effective information processing.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"23"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145849087","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 novel dilated Bi-LSTM framework for depression detection from speech signals through feature fusion. 基于特征融合的扩展Bi-LSTM语音信号抑制检测框架。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-02-03 DOI: 10.1007/s11571-026-10411-9

Uma Jaishankar, Jagannath H Nirmal, Girish Gidaye

{"title":"A novel dilated Bi-LSTM framework for depression detection from speech signals through feature fusion.","authors":"Uma Jaishankar, Jagannath H Nirmal, Girish Gidaye","doi":"10.1007/s11571-026-10411-9","DOIUrl":"https://doi.org/10.1007/s11571-026-10411-9","url":null,"abstract":"A crucial method for determining a person's mental health and assessing their degree of depression is depression detection. To identify depression through speech or conversation, a number of sophisticated methods and questionnaires have been created. The constraints of the current system are as follows: reduced effectiveness as a result of poor feature selection and extraction, problems with interpretability, and the difficulty of identifying depression in different languages. As a result, the proposed model is presented to offer improved accuracy and efficient performance. While adaptive threshold-based pre-processing (AdaT) is used to eliminate quiet and unnecessary information, the twinned Savitzky-Golay filter (TSaG) is used to minimize noise in the dataset. To turn the signal into an image, a Synchro-Squeezed Adaptive Wavelet Transform Algorithm (SSawT) is employed. The Singular Empirical Decomposition and Sparse Autoencoder (SiFE) model is used to extract linear and deep features. Input's deep, linear, and statistical properties are combined using the Weighted Soft Attention-based Fusion (WSAttF) model. From the fused features, the Chaotic Mud Ring Optimization algorithm (ChMR) chooses the best features. A Dilated Convolutional Neural Network (CNN) based Bidirectional-Long Short Term Memory-Bi-LSTM (DiCBiL) is used to detect different stages of depression, which lowers error rates and increases detection accuracy. The proposed method achieves 93.22% of F1-score, 93.11% precision, 93.12% recall, and 93.31% accuracy on the DAIC-WOZ original test set. During the testing time, two more datasets, namely AVEC 2019 and MELD, are used to validate the proposed performance, attaining an accuracy of 93.91% and 85.34% respectively.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10411-9.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"44"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12868482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123895","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

EEG emotion recognition across subjects based on deep feature aggregation and multi-source domain adaptation. 基于深度特征聚合和多源域自适应的脑电情感识别。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2025-11-24 DOI: 10.1007/s11571-025-10379-y

Kunqiang Lin, Ying Li, Yiren He, Zihan Jiang, Renjie He, Xianzhe Wang, Hongxu Guo, Lei Guo

{"title":"EEG emotion recognition across subjects based on deep feature aggregation and multi-source domain adaptation.","authors":"Kunqiang Lin, Ying Li, Yiren He, Zihan Jiang, Renjie He, Xianzhe Wang, Hongxu Guo, Lei Guo","doi":"10.1007/s11571-025-10379-y","DOIUrl":"https://doi.org/10.1007/s11571-025-10379-y","url":null,"abstract":"Electroencephalography (EEG) can objectively reflect an individual's emotional state. However, due to significant inter-subject differences, existing methods exhibit low generalization performance in emotion recognition across different individuals. Therefore, an EEG emotion classification framework based on deep feature aggregation and multi-source domain adaptation is proposed by us. First, we design a deep feature aggregation module that introduces a novel approach for extracting EEG hemisphere asymmetry features and integrates these features with the frequency and spatiotemporal characteristics of the EEG signals. Additionally, a multi-source domain adaptation strategy is proposed, where multiple independent feature extraction sub-networks are employed to process each domain separately, extracting discriminative features and thereby alleviating the feature shift problem between domains. Then, a domain adaptation strategy is employed to align multiple source domains with the target domain, thereby reducing inter-domain distribution discrepancies and facilitating effective cross-domain knowledge transfer. Simultaneously, to enhance the learning ability of target samples near the decision boundary, pseudo-labels are dynamically generated for the unlabeled samples in the target domain. By leveraging predictions from multiple classifiers, we calculate the average confidence of each pseudo-label group and select the pseudo-label set with the highest confidence as the final label for the target sample. Finally, the mean of the outputs from multiple classifiers is used as the model's final prediction. A comprehensive set of experiments was performed using the publicly available SEED and SEED-IV datasets. The findings indicate that the method we proposed outperforms alternative methods.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"8"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12644276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145630828","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

[Formula: see text]AC-Dep: dynamic adaptive feature fusion and domain adaptation collaboration for cross-subject depression detection. [公式：见文]AC-Dep：动态自适应特征融合与领域自适应协同跨学科抑郁检测。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-06-02 DOI: 10.1007/s11571-026-10474-8

Huang Huang, Xinhui Li, Minchao Wu, Rui Ouyang, JingJing Li, Zhao Lv

{"title":"[Formula: see text]AC-Dep: dynamic adaptive feature fusion and domain adaptation collaboration for cross-subject depression detection.","authors":"Huang Huang, Xinhui Li, Minchao Wu, Rui Ouyang, JingJing Li, Zhao Lv","doi":"10.1007/s11571-026-10474-8","DOIUrl":"https://doi.org/10.1007/s11571-026-10474-8","url":null,"abstract":"Depression is a major global public health issue that profoundly deteriorates the quality of life for patients and increases the risk of mortality. Electroencephalogram (EEG) signals, as objective physiological markers, have emerged as a focal point in research for depression detection. However, existing methods suffer from inadequate feature representation and fusion, as well as poor model generalization performance due to individual variability. Inspired by the above observations, we propose a cross-subject depression detection method based on the synergy of dynamic adaptive feature fusion and domain adaptation. Specifically, we design a CNN-Transformer dual-branch structure to separately capture local and global EEG features, and further introduce a lightweight dynamically adaptive attention fusion module to efficiently integrate multi-branch features. Moreover, we propose an end-to-end collaborative optimization framework that unifies feature learning and domain adaptation. By jointly optimizing the source domain classification loss, pseudo-label loss, and domain-adversarial loss, the model extracts discriminative representations while aligning cross-domain distributions, thereby achieving deep coupling between feature discriminability and domain invariance and effectively improving cross-subject generalization. Experiments are conducted on two public datasets. Our method achieves 94.39% and 90.77% accuracy, outperforming the state-of-the-art baselines by 5.94% and 2.75%, respectively.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"104"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161769","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

JDA-RSDB: a multimodal domain adaptation method for cross-session emotion recognition from EEG and eye movement signals. JDA-RSDB：一种基于脑电和眼动信号的多模态域自适应方法。

IF 4.5 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-04-09 DOI: 10.1007/s11571-026-10447-x

Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Gibran Fuentes-Pineda

{"title":"JDA-RSDB: a multimodal domain adaptation method for cross-session emotion recognition from EEG and eye movement signals.","authors":"Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Gibran Fuentes-Pineda","doi":"10.1007/s11571-026-10447-x","DOIUrl":"10.1007/s11571-026-10447-x","url":null,"abstract":"Multimodal emotion recognition has shown growing interest in affective computing, as combining Electroencephalogram (EEG) and eye movement (EM) signals enables the capture of complex emotional processes. However, EEG and EM signals are exposed to joint distribution differences across different days and recorded sessions, reducing the recognition performance. Currently, domain adaptation has been developed to address such distribution differences. Unfortunately, existing domain adaptation solutions still show suboptimal classification results, since ambiguous and non-discriminative decision boundaries are still learned during distribution matching. This paper presents Joint Distribution Alignment with Refined and Separable Decision Boundaries (JDA-RSDB), a multimodal domain adaptation method for cross-session emotion recognition from EEG and EM signals. Our proposed method assumes that a more discriminative feature representation must be ensured on new sessions during joint distribution matching. For this, JDA-RSDB produces similar marginal and conditional distributions between domains, first aligning feature statistics at modality and domain levels, and then, motivating consistent similarity between fused samples from different domains that produce the same class prediction. Simultaneously, this similarity is enhanced by learning a separable feature space on target data, placing decision boundaries on low-density regions. More importantly, decision boundaries are refined by achieving an agreement between target predictions from a principal classifier and those from an auxiliary classifier. Experiments were conducted on three public datasets, SEED-GER, SEED-IV, and SEED-V, in a cross-session setting. The proposed framework achieves an average accuracy of 83.33%, 80.89%, and 75.17% across the three available sessions on SEED-GER, SEED-IV, and SEED-V, outperforming state-of-the-art solutions.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"76"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13066199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671064","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-scroll generation mechanism, dynamic analysis, and DSP implementation of a dual-memristor-coupled Sprott-C system. 一种双忆阻器耦合Sprott-C系统的多涡旋生成机制、动态分析和DSP实现。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-03-13 DOI: 10.1007/s11571-026-10430-6

Yichen Bi, Shuhan Yang, Xianying Xu, Santo Banerjee, Jun Mou

引用次数: 0

A new BCI paradigm based on biological brain - digital twin brain dialogue. 一种基于生物脑-数字双脑对话的BCI新范式。

IF 3.9 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-03-24 DOI: 10.1007/s11571-026-10439-x

Ting Zhang, Rongxin Zhang, Xin Zeng, Min Zeng, Yuanhang Xu, Yue Xiong, Ge Zhang, Daqing Guo, Dezhong Yao

{"title":"A new BCI paradigm based on biological brain - digital twin brain dialogue.","authors":"Ting Zhang, Rongxin Zhang, Xin Zeng, Min Zeng, Yuanhang Xu, Yue Xiong, Ge Zhang, Daqing Guo, Dezhong Yao","doi":"10.1007/s11571-026-10439-x","DOIUrl":"10.1007/s11571-026-10439-x","url":null,"abstract":"Brain-computer interface (BCI) establishes a bidirectional pathway between the brain and external devices. Its applications fall into two main categories: utilizing the brain as a controller (e.g., for prosthetics) or as a modulation target (e.g., for cognitive regulation). Progress in BCI is constrained by two core bottlenecks: in brain control, limited understanding of neural coding mechanisms restricts improvements in the accuracy and robustness of encoding/decoding algorithms; in brain regulation, one-size-fits-all regulatory strategies struggle to address significant individual variability, resulting in heterogeneous therapeutic responses. Inspired by neuroscience advances, this perspective proposes a new biological brain - digital twin brain based BCI (BDBCI) paradigm. Here, the biological brain acts as an empirical anchor and ultimate validation platform, while a high-fidelity digital twin brain (DTB) serves as a theoretical inference engine and virtual testbed. Specifically, experimental induction is applied to the biological brain to distill preliminary conclusions, such as brain-behavior mappings and brain-stimulation causal relationships, which are then used to construct and calibrate the DTB model. Subsequently, on the DTB platform, large-scale model deduction is conducted to validate and deepen these preliminary insights mechanistically, thereby optimizing control/regulation parameters or informing the parameter ranges for the next round of experimental induction and model deduction. Through this BDBCI paradigm, we aim to advance BCI research from empirical trial-and-error toward a new era of model-driven, predictable, and explainable precision science.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"70"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147510091","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

Machine learning-based detection techniques for post-traumatic stress syndrome recognition. 基于机器学习的创伤后应激综合征识别检测技术。

IF 4.5 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-07-11 DOI: 10.1007/s11571-026-10495-3

V Haripriya, Anamika Tiwari, Sumitra Padmanabhan, Swapnil M Parikh, Amandeep Kaur, Jayant Jagtap

{"title":"Machine learning-based detection techniques for post-traumatic stress syndrome recognition.","authors":"V Haripriya, Anamika Tiwari, Sumitra Padmanabhan, Swapnil M Parikh, Amandeep Kaur, Jayant Jagtap","doi":"10.1007/s11571-026-10495-3","DOIUrl":"10.1007/s11571-026-10495-3","url":null,"abstract":"Post-Traumatic Stress Disorder (PTSD) is regarded as a medical condition that affects a large number of individuals worldwide from a variety of professions. However, PTSD diagnosis and acquiring precise diagnosis methods have become more difficult tasks. In this research, a novel Machine Learning (ML) system, the Hybrid Glow-Worm Optimized Multi-Level Spectral Support Vector Machine (HGO-MLSSVM), is proposed for PTSD recognition. The primary objective of the proposed method is to extract and classify the voice frame. The audio dataset was employed to assess and categorize the speech signal-derived PTSD. Following that, pre-processing, feature extraction, and classification are processed to detect PTSD patients through the HGO-MLSSVM technique. The proposed method produces very promising results, obtaining ROC (0.98), specificity (98.02%), sensitivity (97.5%), and accuracy (98.50%) when compared to traditional approaches. Furthermore, this study shows the significant effectiveness of the HGO-MLSSVM technique compared to current methodologies and a discourse on prospective directions for forthcoming PTSD recognition.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"135"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430040","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

HADANet: hybrid attentive domain adaptation for cross-subject motor imagery EEG decoding. HADANet：跨主体运动意象脑电解码的混合注意域自适应。

IF 4.5 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-07-11 DOI: 10.1007/s11571-026-10510-7

Dingming Wu, Meng Tang, Shihong Liu

{"title":"HADANet: hybrid attentive domain adaptation for cross-subject motor imagery EEG decoding.","authors":"Dingming Wu, Meng Tang, Shihong Liu","doi":"10.1007/s11571-026-10510-7","DOIUrl":"10.1007/s11571-026-10510-7","url":null,"abstract":"Motor imagery (MI) based brain-computer interfaces (BCIs) enable direct decoding of human motor intentions from neural activity. Electroencephalography (EEG) is widely used for MI decoding due to its non-invasive nature and high temporal resolution. However, large inter-subject variability in EEG signals leads to significant distribution discrepancies across subjects, which limits the generalization ability of existing decoding models. To address this issue, we propose a Hybrid Attentive Domain Adaptation Network (HADANet) for cross-subject motor imagery EEG decoding. The proposed framework employs a hierarchical convolutional feature extractor to capture complementary temporal and spectral characteristics of EEG signals. A hybrid attention mechanism further enhances discriminative spatial and channel-wise neural representations. In addition, a hybrid domain adaptation strategy combining adversarial learning and multi-kernel maximum mean discrepancy (MMD) alignment is introduced to reduce inter-subject distribution discrepancies and learn domain-invariant features. Experiments on the PhysioNet and Cho motor imagery datasets demonstrate that HADANet achieves competitive performance compared with several state-of-the-art methods, obtaining average accuracies of 82.85% and 85.87%, respectively. The results demonstrate that our framework effectively models motor imagery-related neural patterns and improves cross-subject generalization for practical BCI systems. the code in public https://github.com/curiouspeople/HADANet-.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"137"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430060","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

MountPat: investigations on the EEG signals. MountPat：对脑电图信号的研究。

IF 4.5 3区工程技术

Cognitive Neurodynamics Pub Date : 2026-12-01 Epub Date: 2026-01-24 DOI: 10.1007/s11571-026-10421-7

Ugur Ince, Omer Faruk Goktas, Ilknur Sercek, Serkan Kirik, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer

{"title":"MountPat: investigations on the EEG signals.","authors":"Ugur Ince, Omer Faruk Goktas, Ilknur Sercek, Serkan Kirik, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer","doi":"10.1007/s11571-026-10421-7","DOIUrl":"10.1007/s11571-026-10421-7","url":null,"abstract":"To extract information from the brain, the most cost-effective method is electroencephalography (EEG) signal acquisition. Therefore, many researchers have used EEG signals to capture brain activity. EEG signals are complex; hence, computer-aided models-especially machine learning (ML)-are generally employed to interpret them. The primary objective of this research is to demonstrate the feature-extraction capability of a new, novel method. The proposed feature-extraction approach employs a deterministic feature-engineering transformation, designed to restructure multi-strided signal representations through fixed linear operations. The resulting transformation graph exhibits a mountain-like structure; therefore, we term the model MountPat. To evaluate MountPat's performance, we present an explainable feature engineering (XFE) model with four main phases. In the first phase, we extract informative features using MountPat. In the second phase, we select the most informative features using cumulative weighted iterative neighborhood component analysis (CWNCA). In the third phase, we generate classification results by applying t-algorithm-based k-nearest neighbors (tkNN). In the fourth phase, we extract explainable insights from the EEG signals using the Directed Lobish (DLob) explainable artificial intelligence (XAI) method. To demonstrate the general classification ability of the MountPat-based XFE framework, we use six EEG datasets. Under rigorous subject-independent (LOSO) validation, the model achieves 76.36%-98.88% accuracy, demonstrating strong cross-subject generalization. Sample-wise tenfold CV results exceed 89% on all six datasets. Moreover, by deploying the DLob XAI method, we generate interpretable results for each dataset. These results clearly illustrate that the MountPat-based XFE framework is an effective feature-extraction approach for multichannel signal processing.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"53"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12921105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147270022","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