Cogn Neurodyn [JOURNAL]
Sun
200 papers
Does learning a second or third language affect the adaptation of cognitive control in multilinguals? A longitudinal fMRI study.
Xing Z, Wang X, Huang J … +2 more , Schwieter JW, Liu H
Cogn Neurodyn · 2026 Dec · PMID 41458475 · Full text
UNLABELLED: Numerous studies in the bilingual literature have shown that cognitive control adapts to several factors related to second language (L2) learning. However, whether third language (L3) learning influences cogn...
UNLABELLED: Numerous studies in the bilingual literature have shown that cognitive control adapts to several factors related to second language (L2) learning. However, whether third language (L3) learning influences cognitive control remains underexplored. In this longitudinal study, we analyzed behavioral performance and functional magnetic resonance imaging (fMRI) data among Chinese-English bilinguals at resting-state and during a flanker task both prior to English (L2) or Japanese (L3) learning and one year later. During brain resting-states for these same learners, we conducted a correlation analysis between language exam scores and functional connectivity strength of resting-state data after one year of study. The connectivity between the left anterior cingulate cortex (ACC) and the left precuneus was positively correlated with English listening performance, while the connectivity between the right supramarginal gyrus (SMG) and the right inferior parietal lobe (IPL) was negatively correlated with English oral performance. The behavioral results from the flanker task showed that after one year of L2 learning in a classroom setting, a significantly smaller flanker effect emerged among Chinese-English bilinguals. Moreover, brain imaging revealed that incongruent flanker trials elicited greater activation of the left superior frontal gyrus (SFG) than congruent trials. These behavioral and neural patterns were not found among Chinese-English bilinguals who had studied Japanese for one year. Taken together, these findings suggest that cognitive control adapts to L2 learning, but appears to be unaffected by L3 learning. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10397-w.
Fractal Transition and Neuromorphic Physiology of Vanadium Dioxide-Memristor under a FractionalDifferential Framework.
Abro KA, Souayeh B
Cogn Neurodyn · 2026 Dec · PMID 41458474 · Full text
Vanadium dioxide is a well-known candidate for memristor applications due to its insulator-to-metal transition characteristics, this is because vanadium dioxide memristors are versatile devices whose operating mechanism...
Vanadium dioxide is a well-known candidate for memristor applications due to its insulator-to-metal transition characteristics, this is because vanadium dioxide memristors are versatile devices whose operating mechanism is based on an abrupt and volatile change of resistivity. This manuscript introduces the fractal-fractional framework for a third-order vanadium dioxide memristor neuron model that investigates the role of non-local dynamics on chaotic behavior. The third-order vanadium dioxide memristor neuron model is analyzed under three conditions of fractal-fractional differential operators (i) deviation of fractional parameter with fixed fractal order, (ii) deviation of fractal parameter with fixed fractional order, and (iii) simultaneous deviation of both parameters. The mathematical model of third-order vanadium dioxide memristor neuron has been discretized by means of Adams-Bashforth-Moulton method for the sake of numerical simulations. The results highlight the fractal-fractional framework as a versatile tool for tailoring vanadium dioxide memristor neuron's dynamics namely irregular oscillations, dispersed attractors with enhanced chaoticity, bounded loops with tunable stability and excessive fluctuations. These findings confirm that fractional order acts as a memory controller, while fractal order governs structural scaling, together enabling precise modulation between chaos and stability.
C2DGCN: cross-connected distributive learning-enabled graph convolutional network for human emotion recognition using electroencephalography signal.
Cholke P, Uke S, Chavhan JJ … +3 more , Kulkarni AM, Chandolikar N, Gadhave RT
Cogn Neurodyn · 2026 Dec · PMID 41458473 · Full text
Emotion Recognition generally involves the identification of the present mental state or psychological conditions of the human while interacting with others. Among the various modalities, Electroencephalography is the mo...
Emotion Recognition generally involves the identification of the present mental state or psychological conditions of the human while interacting with others. Among the various modalities, Electroencephalography is the most deceptive emotion recognition technique because of its ability to characterize brain activities accurately. Several emotion recognition methods have been designed utilizing Deep Learning approaches from EEG signals. Yet, their inability to capture the complex features and the occurrence of the overfitting problems with increased computational complexity affected their extensive application. Therefore, this research proposes the Cross-Connected Distributive Learning-enabled Graph Convolutional Network (C2DGCN) for effective emotion recognition. Specifically, the cross-connected distributive learning in the C2DGCN enables extensive feature sharing and integration, thus reducing the computation complexity and improving the accuracy. Further, the application of the Statistical Time-Frequency Signal descriptor aids in the extraction of complex features and mitigates the overfitting issue. The experimental validation revealed the effectiveness of the C2DGCN by achieving a high accuracy of 97.73%, sensitivity of 98.32%, specificity of 98.22%, and precision of 98.32% with 90% of training using the SEED-IV dataset. For the evaluation using the DEAP dataset, the proposed C2DGCN model reaches an accuracy of 97.66%, precision of 97.98%, sensitivity of 97.25%, and specificity of 98.07%.
EEG emotion recognition based on hierarchical multi-scale graph neural networks.
Gu W, Peng J, Ma S … +2 more , Li X, Zou Y
Cogn Neurodyn · 2026 Dec · PMID 41458472 · Full text
With the development of emotion recognition technology in various applications, studies based on EEG signals were carried out as they can directly reflect brain activity. Although existing graph neural network (GNN) meth...
With the development of emotion recognition technology in various applications, studies based on EEG signals were carried out as they can directly reflect brain activity. Although existing graph neural network (GNN) methods have made some progress in processing EEG signals, they still face significant limitations in capturing complex spatiotemporal dependencies, avoiding over-smoothing, and handling cross-regional brain signal interactions, which impact the accuracy and robustness of emotion recognition. To address these problems, this paper proposes a Hierarchical Multi-Scale Graph Neural Network (HMSGNN). This method enhances the spatiotemporal feature modeling ability of EEG signals by extracting features at multiple levels, from local to global, thus improving the accuracy and robustness of emotion recognition. Experimental results show that HMSGNN achieves recognition accuracies of 98.67% and 85.72% in subject-dependent experiments on the SEED and SEED-IV datasets, and 87.11% and 76.14% in subject-independent experiments, respectively. Under the reproduced experimental settings, these values are the highest among the compared methods, while maintaining comparable or lower variance.
Short-term and long-term test-retest reliability of memory, complexity, and randomness of EEG microstates sequence.
Tarailis P, Artoni F, Koenig T … +2 more , Michel CM, Griskova-Bulanova I
Cogn Neurodyn · 2026 Dec · PMID 41383564 · Full text
UNLABELLED: EEG microstates sequence analysis gained a lot of attention in recent years and different sequence analysis methods have been applied to study microstates sequence randomness, complexity, speed, periodicity,...
UNLABELLED: EEG microstates sequence analysis gained a lot of attention in recent years and different sequence analysis methods have been applied to study microstates sequence randomness, complexity, speed, periodicity, and long-range memory. Although several studies have reported on the reliability of temporal parameters, the stability of sequence-based metrics within subjects has not yet been systematically examined. In this study, we analysed EEG recordings from 60 healthy young adults and assessed short-term (90 min) and long-term (30 days) test-retest reliability and agreement of sequence measures: long-range memory (Hurst exponent), complexity (two Lempel-Ziv algorithms), and randomness (entropy and entropy rate). Across metrics, short-term reliability was consistently good to excellent (ICC = 0.831-0.902), and long-term reliability was moderate to good (ICC = 0.651-0.793). Entropy and entropy rate emerged as the most stable measures across both intervals, confirmed by minimal bias and strong agreement. These findings demonstrate that EEG microstate sequence dynamics represent a stable trait of neural activity, providing a solid methodological foundation for future studies that aim to embed these metrics into computational models and explore their translational value as neurophysiological biomarkers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10391-2.
DCPat-XFE: an explainable EEG model for psychogenic nonepileptic seizure detection.
Unal DA, Tanko D, Sercek I … +8 more , Tasci I, Tuncer I, Tasci B, Tasci G, Kaya T, Barua PD, Dogan S, Tuncer T
Cogn Neurodyn · 2026 Dec · PMID 41383563 · Full text
Detecting Psychogenic Nonepileptic Seizures (PNES) is vital because PNES mimics epileptic seizures but has psychological-not electrical-origins, leading to frequent misdiagnosis and ineffective treatment. Electroencephal...
Detecting Psychogenic Nonepileptic Seizures (PNES) is vital because PNES mimics epileptic seizures but has psychological-not electrical-origins, leading to frequent misdiagnosis and ineffective treatment. Electroencephalography (EEG) provides a non-invasive view of brain activity for distinguishing PNES from true epilepsy. Current PNES detection methods remain limited. This study introduces a curated PNES EEG dataset and a novel explainable feature-engineering (XFE) model. Expert neurologists annotated three classes: Normal, PNES with Verbal Suggestion Provocation (VSP+), and PNES without VSP (VSP -). The introduced explainable feature engineering (XFE) framework includes four components: (i) Distance Counter Pattern (DCPat) for channel-pair feature extraction (190 features for 20 channels), (ii) Cumulative Weight-based Neighborhood Component Analysis (CWNCA) for feature selection (threshold = 0.99), (iii) t-algorithm k-Nearest Neighbors (tkNN) ensemble classifier with Iterative Majority Voting (IMV) and greedy optimization, and (iv) Directed Lobish (DLob) for symbolic interpretation and cortical connectome mapping. For this research, we curated an EEG dataset and four cases are created using the curated dataset. These four cases are: Case 1 (Normal vs. PNES VSP+), Case 2 (Normal vs. PNES VSP-), Case 3 (PNES VSP + vs. PNES VSP-), and Case 4 (all three classes).). The introduced DCPat XFE framework reached accuracy above 96.5% in all four cases; Case 2 attained the best overall value (99.11%). DLob strings and connectome diagrams provided clear symbolic explanations of PNES-related patterns. The DCPat-based XFE framework yields high accuracy and interpretable outputs for PNES detection on EEG. These results support its use as a reliable, explainable tool for clinical decision support.
Clearance mechanisms of the glymphatic/lymphatic system in the brain: new therapeutic perspectives for cognitive impairment.
He Z, Sun J
Cogn Neurodyn · 2025 Dec · PMID 41376656 · Full text
The glymphatic/lymphatic system of the brain has been an important discovery in the field of neuroscience in recent years. As the "waste clearance network" of the central nervous system, it clears metabolic products and...
The glymphatic/lymphatic system of the brain has been an important discovery in the field of neuroscience in recent years. As the "waste clearance network" of the central nervous system, it clears metabolic products and neurotoxic substances through the cerebrospinal fluid-interstitial fluid circulation, which is crucial for maintaining the homeostasis of the intracerebral environment and plays important roles in learning, memory and other advanced cognitive functions. The glymphatic/lymphatic system is crucial for the clearance of beta-amyloid and tau proteins, and thus the abnormal function of this system has been confirmed to be closely related to the pathological mechanisms of various diseases associated with cognitive impairment, such as Alzheimer's disease (AD), Parkinson's disease (PD), and vascular dementia. The physiological function of this system is influenced by a variety of factors, especially when it is relatively active during sleep. The application of noninvasive imaging techniques to assess glymphatic/lymphatic system function has facilitated the development of clinical research. Therefore, a focus on the role of the cerebral glymphatic/lymphatic system in cognitive impairment and an understanding of its relationship with cognitive impairment from a new perspective are of great scientific and clinical importance.
A neuro-inspired visual SLAM approach using AKAZE feature extraction in complex and dynamic environments.
Li R, Wang Y, Xu X … +3 more , Li F, Tang F, Pan X
Cogn Neurodyn · 2026 Dec · PMID 41362310 · Full text
Place cells and head direction cells in the rodent brain encode spatial position and orientation, forming the neural basis for navigation and cognitive map construction. Inspired by these mechanisms, RatSLAM simulates th...
Place cells and head direction cells in the rodent brain encode spatial position and orientation, forming the neural basis for navigation and cognitive map construction. Inspired by these mechanisms, RatSLAM simulates their roles to achieve biologically inspired visual SLAM. However, traditional RatSLAM struggles with robust feature extraction in visually complex or dynamic environments, where features may be unstable or non-distinct. To address this, we integrate the AKAZE algorithm into the RatSLAM framework. AKAZE combines accelerated techniques with nonlinear diffusion filtering to construct a multi-scale nonlinear scale space, enabling efficient extraction of robust, scale-invariant features across spatial scales. These features are incorporated into RatSLAM's local view module to improve loop closure detection and mitigate odometry drift. Traditional evaluation approaches rely on real-time pose trajectories and cannot evaluate the trajectories based on the fully optimized experience maps, leading to inaccurate mapping performance assessments. Thus, we further propose a novel Ray-Based Map Metric Error Evaluation Method, which can directly compare the final experience maps generated by RatSLAM. Experiments on the KITTI dataset demonstrate that, compared with both ORB-RatSLAM and the ORB-SLAM3, the proposed AKAZE-RatSLAM achieves higher loop closure recall and mapping accuracy while maintaining a lightweight computational profile. In particular, CPU and memory measurements show that AKAZE-RatSLAM requires significantly less computational resources than ORB-SLAM3, confirming its suitability for real-time deployment on resource-limited robotic platforms. Furthermore, neuro-inspired analyses reveal that the pose cell network exhibits spatially localized and direction-selective firing patterns analogous to hippocampal place cells and head direction cells in rodents. Specifically, cells along the same row encode adjacent spatial regions, forming continuous place-field-like activations, whereas cells in the same column show distinct preferred orientations, indicating directional tuning. These biological characteristics confirm that the proposed AKAZE-RatSLAM not only enhances mapping performance and efficiency but also preserves the neurobiological plausibility of spatial representation, advancing the development of brain-inspired visual SLAM systems.
Attention-guided deep learning-machine learning and statistical feature fusion for interpretable mental workload classification from EEG.
Majumder S, Patra D, Gorai S … +2 more , Halder A, Biswas U
Cogn Neurodyn · 2026 Dec · PMID 41362309 · Full text
Accurate assessment of mental workload (MWL) from electroencephalography (EEG) signals is crucial for real-time cognitive monitoring in safety-critical domains such as aviation and human-computer interaction. Although va...
Accurate assessment of mental workload (MWL) from electroencephalography (EEG) signals is crucial for real-time cognitive monitoring in safety-critical domains such as aviation and human-computer interaction. Although various computational approaches have been proposed, those mostly suffer from limited robustness, interpretability, or fail to fully exploit both temporal and non-linear neural dynamics. This article introduces a novel hybrid deep learning and XGBoost stacking ensemble framework for reliable and interpretable MWL classification from EEG. The proposed pipeline systematically includes preprocessing of raw EEGs, followed by comprehensive feature extraction (time-domain, frequency-domain, wavelet-based, entropy, and fractal dimension features), and subsequent discriminative feature selection phase using ANOVA F-values, yielding a compact set of 200 highly informative features. The proposed architecture consists of dual processing branches: a CNN-BiLSTM-Attention based deep learning branch for automatic learning of spatiotemporal dynamics, and an XGBoost branch for robust classification from engineered features. Predictions from both branches are integrated using a logistic regression stacking ensemble, maximizing complementary strengths and improving generalization. Experiments are conducted on the STEW (simultaneous workload) and EEGMAT (mental arithmetic task) dataset. Proposed model yields 96.87% and 99.40% of classification accuracy by outperforming 16 and 7 previously published state-of-the-art techniques on STEW and EEGMAT dataset respectively. Attention heatmaps and SHAP value analysis provide intuitive visual explanations and interpretability of the model's decision making, while systematic ablation studies validate the contribution of each architectural module. This work demonstrates that a carefully engineered stacking ensemble, informed by both deep learning and classical machine learning, capable of delivering not only improved performance but also enhanced interpretability for EEG-based MWL assessment in real-world applications.
Multimodal biometric authentication systems: exploring EEG and signature.
Das BB, Reddy CV, Matha U … +2 more , Yandapalli C, Ram SK
Cogn Neurodyn · 2026 Dec · PMID 41362308 · Full text
Biometric traits are unique characteristics of an individual's body or behavior that can be used for identification and authentication. Biometric authentication uses unique physiological and behavioral traits for secure...
Biometric traits are unique characteristics of an individual's body or behavior that can be used for identification and authentication. Biometric authentication uses unique physiological and behavioral traits for secure identity verification. Traditional unimodal biometric authentication systems often suffer from spoofing attacks, sensor noise, forgery, and environmental dependencies. To overcome these limitations, our work presents multimodal biometric authentication integrated with the characteristics of electroencephalograph (EEG) signals and handwritten signatures to enhance security, efficiency, and robustness. EEG-based authentication uses the brainwave patterns' intrinsic and unforgeable nature, while signature recognition demonstrates an additional behavioral trait for effectiveness. Our system processes EEG data of an individual with 14-channel readings, and the signature with the images ensures a seamless fusion of both modalities.Combining physiological and behavioral biometrics, our approach will significantly decrease the risk of unimodal authentication, including forgery, spoofing, and sensor failures. Our system, evaluated on a dataset of 30 subjects with genuine and forged data, demonstrates a 97% accuracy. Designed for small organizations, the modular structure, low computation algorithms, and simplicity of the hardware promote deployment scalability.
Tremor estimation and filtering in robotic-assisted surgery.
Jia B, Wang W, Tian X … +1 more , Wang X
Cogn Neurodyn · 2026 Dec · PMID 41362307 · Full text
In surgical procedures, surgeons can suffer from spontaneous hand tremors that can affect the accuracy of surgical robots. Therefore, it is necessary to measure and model the tremor signal by sensors to suppress hand tre...
In surgical procedures, surgeons can suffer from spontaneous hand tremors that can affect the accuracy of surgical robots. Therefore, it is necessary to measure and model the tremor signal by sensors to suppress hand tremor. This paper proposes a prediction method based on deep learning that integrates long-term and short-term features to achieve this goal. The long-term features of tremor signals are extracted using a bidirectional Long-short-term memory network, while the short-term features are extracted using a Temporal Convolutional Network. By integrating the long-term and short-term characteristics of tremor signals, this approach provides rich temporal information for signal estimation. In addition, genetic algorithm is used to obtain the optimal time step-size to fully explore the temporal correlation of signals, and an end data compensation strategy is adopted to ensure that the tremor filtering covers the entire process. The performance of the proposed method is evaluated by training and testing on the same dataset as other methods, and conducting suture experiments in a virtual surgical environment. The results show that our proposed model is superior to the existing methods, effectively reducing the tremor signals estimation error. This method can provide better tremor estimation and compensation performance, effectively suppressing the hand tremors and improving the surgical accuracy.
Irreversibility of recursive Heaviside memory functions: a distributional perspective on structural cognition.
Shin C
Cogn Neurodyn · 2026 Dec · PMID 41321655 · Full text
Modern AI systems excel at pattern recognition and task execution, but they often fall short of replicating the layered, self-referential structure of human thought that unfolds over time. In this paper, we present a mat...
Modern AI systems excel at pattern recognition and task execution, but they often fall short of replicating the layered, self-referential structure of human thought that unfolds over time. In this paper, we present a mathematically grounded and conceptually simple framework based on smoothed step functions-sigmoid approximations of Heaviside functions-to model the recursive development of mental activity. Each cognitive layer becomes active at a specific temporal threshold, with the abruptness or gradualness of activation governed by an impressiveness parameter [Formula: see text], which we interpret as a measure of emotional salience or situational impact. Small values of [Formula: see text] represent intense or traumatic experiences, producing sharp and impulsive responses, while large values correspond to persistent background stress, yielding slow but sustained cognitive activation. We formulate the recursive dynamics of these cognitive layers and demonstrate how they give rise to layered cognition, time-based attention, and adaptive memory reinforcement. Unlike conventional memory models, our approach captures thoughts and recall events through a recursive, impressiveness-sensitive pathway, leading to context-dependent memory traces. This recursive structure offers a new perspective on how awareness and memory evolve over time, and provides a promising foundation for designing artificial systems capable of simulating recursive, temporally grounded consciousness.
Leveraging Swin Transformer for advanced sentiment analysis: a new paradigm.
Rajput GK, Srivastava SK, Gupta N
Cogn Neurodyn · 2026 Dec · PMID 41321654 · Full text
As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer-BiLSTM-Spatial ML...
As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer-BiLSTM-Spatial MLP (Swin-MLP) model that leverages hierarchical attention, shifted-window mechanisms, and spatial MLP layers to extract features from domain-specific healthcare text better. The framework is tested on domain-specific datasets for Drug Review and Medical Text, and performance is assessed against baseline models (BERT, LSTM, and GRU). Our findings show that the Swin-MLP model performs significantly better overall, achieving superior metrics (accuracy, precision, recall, F1-score, and AUC) and improving mean accuracy by 1-2% over BERT. Statistical tests to assess significance (McNemar's test and paired t-test) indicate that improvements are statistically significant (p < 0.05), suggesting the efficacy of the architectural innovations. The results' implications indicate that the model is robust, efficiently converges to classification, and is potentially helpful for a wide range of domain-specific sentiment analyses in healthcare. We will examine future research directions into exploring lightweight attention mechanisms, cross-domain multimodal sentiment analysis, federated learning to protect privacy, and hardware implications for rapid training and inference.
EEG emotion recognition across subjects based on deep feature aggregation and multi-source domain adaptation.
Lin K, Li Y, He Y … +5 more , Jiang Z, He R, Wang X, Guo H, Guo L
Cogn Neurodyn · 2026 Dec · PMID 41306194 · Full text
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...
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.
Novel contrastive representation learning of epileptic electroencephalogram for seizure detection.
Wang J, Wang Y, Tang Q … +5 more , Zeng X, Zhai D, Xiao H, Nie W, Yuan Q
Cogn Neurodyn · 2026 Dec · PMID 41306193 · Full text
Detecting seizures automatically is crucial for diagnosing and treating epilepsy, substantially benefiting affected patients. Various deep learning models and methods have been developed to automatically extract features...
Detecting seizures automatically is crucial for diagnosing and treating epilepsy, substantially benefiting affected patients. Various deep learning models and methods have been developed to automatically extract features from electroencephalogram (EEG) data for detecting seizures, but may often fail to adequately capture the significant periodic and semi-periodic dynamics in EEG signals, thus incompletely representing the extracted features. To address this challenge, we here introduced a novel EEG feature learning framework named ContrLF. This framework combines a contrastive learning framework and the Floss method to improve EEG feature learning for epileptic seizure detection. In our methodology, initially, both strong and weak augmentation are applied to transform the original EEG data into two distinct yet correlated views. Then, Floss is employed to automatically detect and learn the primary periodic dynamics within the augmented EEG data, capturing meaningful periodic representations that are essential for understanding seizure patterns in EEG signals. In parallel, the augmented EEG data were sequentially processed through temporal and contextual contrasting modules, which are designed to learn robust feature representations of the EEG signals. Finally, a Support Vector Machine (SVM) classifier was used to evaluate the effectiveness of the EEG features extracted using our proposed framework. Experimental results generated using both scalp and intracranial electroencephalogram (iEEG) datasets revealed that the proposed framework achieves over 90% accuracy, sensitivity, and specificity in detecting seizures. The framework outperforms other state-of-the-art methods, demonstrating its superiority in both cross-patient and specific-patient seizure detection.
Control analysis of deep brain stimulation and optogenetics for Alzheimer's disease under the computational cortex model.
Zhang Y, Zhang H, Shen Z
Cogn Neurodyn · 2026 Dec · PMID 41306192 · Full text
Abnormal τ and β-amyloid (Aβ) deposition in the brains of patients with Alzheimer's disease (AD) is significantly associated with cognitive decline. This abnormal deposition has been reported to be linked to increased ex...
Abnormal τ and β-amyloid (Aβ) deposition in the brains of patients with Alzheimer's disease (AD) is significantly associated with cognitive decline. This abnormal deposition has been reported to be linked to increased excitatory and inhibitory time constants in neural circuits. In this paper, we focus on three typical electroencephalography (EEG) slowdowns clinically reported in association with AD, including decreased dominant frequency, decreased rhythmic activity, and increased δ + θ rhythmic activity. Firstly, we demonstrate that changes in excitatory time constant, inhibitory time constants, and synaptic connection strength can induce the phenomenon of EEG slowdowns in early AD. Then, we are interested in the regulation of AD by traditional deep brain stimulation (DBS) and emerging optogenetic stimulation. High-frequency, high-pulse width, and high-amplitude DBS are more effective in reversing brain rhythm in AD, supporting the experiment that cortical high-frequency DBS may be an effective therapeutic way for dementia-related diseases. In particular, as a modification of traditional DBS, we find that oscillatory bursty stimulation can compensate for the shortcomings of DBS at low amplitude. However, it is physiologically difficult to target inhibitory interneurons with conventional electrical stimulation. Optogenetics is able to precisely stimulate pyramidal neurons and inhibitory interneurons observed in animal experiments. Our numerical results indicate that medium and low-frequency stimulation can better eliminate AD pathology. It should be noted that stimulation of inhibitory interneurons requires greater light intensity than stimulation of pyramidal neurons. Finally, we propose two optimization intermittent optogenetic stimulation protocols. These modeling results can reproduce some experimental phenomena and are expected to reveal the underlying pathological mechanisms and control strategies associated with cognitive dysfunction such as AD.
Discrete memristive spiking neural networks: investigating information flow, synchronization, and emergent intelligence.
He S, Xiao J, Peng Y … +1 more , Wang H
Cogn Neurodyn · 2026 Dec · PMID 41306191 · Full text
The processing of information within complex neural networks is a challenge topic that has intrigued researchers for many years. In this paper, we conducted an in-depth investigation into the learning mechanisms that are...
The processing of information within complex neural networks is a challenge topic that has intrigued researchers for many years. In this paper, we conducted an in-depth investigation into the learning mechanisms that are intrinsic to discrete memristor spiking neural networks. We also explored the effectiveness of information transmission and synchronization among various neurons and networks. Firstly, a memristor model with memory regulation function and tanh function's nonlinear characteristics was constructed. This model not only ensures that the internal state variables of the memristor do not exhibit divergence, but also demonstrates that this memristor is suitable for spiking signal processing and has the ability to transmit spiking signals. Secondly, our research delved into the intricate dynamics of these discrete spiking neural networks, including the ternary coupled spiking neural network and ring coupled spiking neural network, aiming to shed light on how they operate and interact. Thirdly, based on the designed pulse neurons, this study constructed a simple pulse neuron network. By reasonably setting the relevant parameters, the research found that this network possesses the ability for pattern recognition. The results of our investigation are crucial for understanding the mechanisms of information processing and synchronization phenomena within neural networks. It provides valuable insights into the potential of memristor networks in advancing artificial intelligence and computational neuroscience.
Bio-inspired spiking neural network for modeling and optimizing adaptive vertigo therapy.
N V, K R, Nallu Vivekanandan YK
Cogn Neurodyn · 2026 Dec · PMID 41306190 · Full text
UNLABELLED: Vertigo, a prevalent neurovestibular disorder, arises from dysfunction in the vestibular system and often lacks precise, personalized treatments. This study proposes a bio-inspired spiking neural network (SNN...
UNLABELLED: Vertigo, a prevalent neurovestibular disorder, arises from dysfunction in the vestibular system and often lacks precise, personalized treatments. This study proposes a bio-inspired spiking neural network (SNN) model that simulates vestibular dysfunction and adaptive recovery using Leaky Integrate-and-Fire (LIF) neurons with spike-timing-dependent plasticity (STDP). The architecture mimics the vestibular pathway through biologically plausible layers: hair cells, afferents, and cerebellar integrators, and models pathological states such as hair cell hypofunction and synaptic disruption. A reinforcement-based feedback mechanism enables the simulation of therapy-induced plasticity, resulting in a 48-62% drop and 38% recovery in cerebellar spike activity during adaptation epochs. The model demonstrates real-time feasibility, with an average simulation runtime of 4 s per epoch on standard hardware. Its design is scalable and well-suited for future deployment on neuromorphic platforms (e.g., Loihi, SpiNNaker). Its modular and interpretable design enables in silico testing of rehabilitation strategies, real-time monitoring of dysfunction, and future personalization using clinical datasets. This work establishes a computational foundation for AI-driven vestibular therapy that is adaptive, explainable, and hardware compatible. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10368-1.
A dual brain EEG examination of the effects of direct and vicarious rewards on bilingual Language control.
Huang J, Liu S, Lv M … +2 more , Schwieter JW, Liu H
Cogn Neurodyn · 2026 Dec · PMID 41245999 · Full text
Little is known about whether direct and vicarious rewards affect bilingual language control in social learning. We used a dual-electroencephalogram (EEG) to simultaneously record the effects of direct and vicarious rewa...
Little is known about whether direct and vicarious rewards affect bilingual language control in social learning. We used a dual-electroencephalogram (EEG) to simultaneously record the effects of direct and vicarious rewards on language control when bilinguals switched between their two languages. We found that both direct and vicarious rewards elicited more switch behavior. On an electrophysiological level, although both direct and vicarious rewards elicited Reward-positivity and Feedback-P3 when receiving reward outcomes, direct rewards induced greater reward effects than vicarious rewards. In addition to an N2 effect in language switching, vicarious rewards elicited more pronounced LPCs relative to direct rewards. More important, in the alpha band, there was a predictive effect of behaviors on rewards in binding vicarious rewards and language switching activities. These findings demonstrate that both direct and vicarious rewards influence language control during language selection.
Real-time driver activity detection using advanced deep learning models.
Al Emran M, Islam MA, Khan MO … +5 more , Rana MJ, Adrita ST, Ahmed MA, Eid MMA, Rashed ANZ
Cogn Neurodyn · 2026 Dec · PMID 41245998 · Full text
Traffic accidents usually result from driver's inattention, sleepiness, and distraction, posing a substantial danger to worldwide road safety. Advances in computer vision and artificial intelligence (AI) have provided ne...
Traffic accidents usually result from driver's inattention, sleepiness, and distraction, posing a substantial danger to worldwide road safety. Advances in computer vision and artificial intelligence (AI) have provided new prospects for designing real-time driver monitoring systems to reduce these dangers. In this paper, we assessed four known deep learning models, MobileNetV2, DenseNet201, NASNetMobile, and VGG19, and offer a unique Hybrid CNN-Transformer architecture reinforced with Efficient Channel Attention (ECA) for multi-class driver activity categorization. The framework defines seven important driving behaviors: Closed Eye, Open Eye, Dangerous Driving, Distracted Driving, Drinking, Yawning, and Safe Driving. Among the baseline models, DenseNet201 (99.40%) and MobileNetV2 (99.31%) achieved the highest validation accuracies. In contrast, the proposed Hybrid CNN-Transformer with ECA attained a near-perfect validation accuracy of 99.72% and further demonstrated flawless generalization with 100% accuracy on the independent test set. Confusion matrix studies further indicate a few misclassifications, verifying the model's high generalization capacity. By merging CNN-based local feature extraction, attention-driven feature refinement, and Transformer-based global context modeling, the system provides both robustness and efficiency. These findings show the practicality of using the suggested technology in real-time intelligent transportation applications, presenting a viable avenue toward reducing traffic accidents and boosting overall road safety.
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