Cogn Neurodyn [JOURNAL]
Sun
200 papers
[Formula: see text]AC-Dep: dynamic adaptive feature fusion and domain adaptation collaboration for cross-subject depression detection.
Huang H, Li X, Wu M … +3 more , Ouyang R, Li J, Lv Z
Cogn Neurodyn · 2026 Dec · PMID 42245905 · Full text
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,...
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.
Lateralization in scalp EEG brain connectivity during hand motor imagery can improve task classification for brain-computer interfaces.
Delavari F, Santaniello S
Cogn Neurodyn · 2026 Dec · PMID 42226947 · Full text
This study evaluates brain connectivity reorganization during motor imagery (MI) tasks and assesses the predictive value of EEG-based functional connectivity measures for MI classification compared to µ-band (8-13 Hz) po...
This study evaluates brain connectivity reorganization during motor imagery (MI) tasks and assesses the predictive value of EEG-based functional connectivity measures for MI classification compared to µ-band (8-13 Hz) power spectrum of selected EEG channels, which are commonly used in MI decoders. We analyzed left- and right-hand MI EEG data from the BCI Competition IV 2a (BCI-IV-2a) and PhysioNet Motor Imagery (PHYS-MI) datasets. Phase Locking Value (PLV), cross-correlation (CC), weighted Phase Lag Index (wPLI), and Granger causality (GC) were evaluated as connectivity measures, and their decoding performance was compared against µ-band power features using Random Forest classifiers. Feature importance and graph-theoretical metrics were also used to examine node relevance, edge contributions, and global network topology across MI conditions. We found that PLV yields the most reliable MI decoding performance across both datasets, with accuracy comparable to power (65.3 ± 11.0% vs. 61.3 ± 11.0% and 58.4 ± 9.9% vs. 58.6 ± 15.7%, mean ± std. dev. across subjects for BCI-IV-2a and PHYS-MI, respectively). Moderate correlation ( = 0.62 and 0.40 for BCI-IV-2a and PHYS-MI, respectively) was found between the mean difference in PageRank centrality of the nodes of the PLV-based network in left- vs. right-hand MI and the Gini importance score of the single-channel power values. Also, while the PLV-based network topology remained stable over time, a small set of connections (7.8 ± 4.5% and 3.1 ± 2.5% of edges) lateralized to the hemisphere contralateral to the movement altered considerably and enhanced classification accuracy by 6.7 ± 5.6% and 16.3 ± 7.5% across subjects. These findings suggest that MI primarily modulates a limited number of task-specific functional connections. Rather than replacing established power-based approaches, connectivity measures provide complementary, network-level insight into how MI-related information is organized, which may inform interpretable feature selection and the design of future brain-computer interface models.
Dynamic task-related prefrontal functional networks evolved in Stroop Color-Word tasks measured by fNIRS.
Ren H, Dai C
Cogn Neurodyn · 2026 Dec · PMID 42212281 · Full text
UNLABELLED: Numerous studies have made significant contributions to understanding resting-state brain networks, advancing the field of neuroscience. Studying dynamic functional connectivity is essential for capturing the...
UNLABELLED: Numerous studies have made significant contributions to understanding resting-state brain networks, advancing the field of neuroscience. Studying dynamic functional connectivity is essential for capturing the temporal evolution of brain network organization. However, investigations of task-related functional networks, especially those assessed through dynamic connectivity during continuous cognitive tasks, remain relatively sparse. This study aims to investigate the temporal dynamics of functional prefrontal networks during continuous cognitive control through dynamic functional connectivity analysis. In contrast to conventional methods that primarily focus on identifying static spatial connectivity patterns, this study applies temporal group independent component analysis (TG-ICA) to functional near-infrared spectroscopy (fNIRS) data acquired during a continuous Stroop Color-Word task. This approach enables the identification of temporally evolving functional connectivity networks at the group level. The results revealed three distinct and interpretable prefrontal network components, including a left-lateralized system for early rule implementation and executive control, a right-dominant network for conflict monitoring and attentional reallocation, and a bilateral network supporting sustained goal maintenance and cognitive stability. These networks exhibited time-varying engagement aligned with different stages of cognitive control during continuous tasks. The findings highlight the utility of TG-ICA in capturing the spatiotemporal characteristics of functional brain networks and offer new insights into the dynamic organization of the prefrontal cortex during executive functioning. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10467-7.
Dynamic central-peripheral balance in brain-muscle interactions reveals motor impairment in post-stroke hemiplegia: an exploratory study.
Yu H, Wang J, Li Q … +10 more , Xu P, Xu S, Chen C, Lu J, Li F, Yao D, Xu P, Hou J, Ma X, Yi C
Cogn Neurodyn · 2026 Dec · PMID 42212280 · Full text
Hemiplegia following stroke is characterized by disrupted neuromuscular interactions, yet the central-peripheral dynamics remain unclear. This study investigated dynamic causal interactions between electroencephalography...
Hemiplegia following stroke is characterized by disrupted neuromuscular interactions, yet the central-peripheral dynamics remain unclear. This study investigated dynamic causal interactions between electroencephalography (EEG) and electromyography (EMG) using the adaptive directed transfer function (ADTF) during a thumb-pressing task in hemiplegic patients and explored the central-peripheral balance between central motor commands and peripheral sensory feedback. Results suggested that patients with better motor functions may exhibit a dynamic transition from relatively balanced bidirectional interactions to centrally dominated descending control and back to balance. Patients with more severe hemiplegia exhibited pronounced descending control impairment and ascending feedback enhancement, particularly on the affected side. The difference between the out-degrees of central-peripheral pathways during the motor preparatory phase served as a potential predictor of motor function, as assessed by the Barthel Index. This finding provides exploratory evidence for the imbalance between peripheral-to-central and central-to-peripheral coupling as a potential neural biomarker for functional recovery, tentatively supporting the development of more targeted and personalized rehabilitation strategies.
Redefining spiking neural networks through the lens of dynamical superspace.
Chen G, Qi J, Liu X … +8 more , Fan Q, Gao Z, Xu S, Wang X, Wu Y, Yang D, Zhou D, Huang T
Cogn Neurodyn · 2026 Dec · PMID 42212279 · Full text
The convergence of neuroscience and artificial intelligence has positioned Spiking Neural Networks (SNNs) as one of the pivotal paradigms for future computing. However, the field faces a theoretical challenge: reconcilin...
The convergence of neuroscience and artificial intelligence has positioned Spiking Neural Networks (SNNs) as one of the pivotal paradigms for future computing. However, the field faces a theoretical challenge: reconciling the mathematical clarity of static deep learning with the rich, non-equilibrium dynamics of biological circuits. We introduce the dynamical superspace, a framework that reimagines neural computing as a continuous hierarchy defined by temporal density and state-space complexity. We suggest that while current synchronous SNNs successfully optimize rate-based equilibria, they often neglect the intrinsic power of biological time. The true neuromorphic advantage emerges by ascending to asynchronous timing, where information is decoupled from clock cycles, and to complex non-equilibrium dynamics, where heterogeneity and criticality drive computation through transient trajectories. We propose a roadmap to bridge global optimization with local execution, leveraging evolutionary priors to support innate learning. By identifying native applications, from ultra-low-latency event perception to infinite-context memory for AGI, this perspective invites the community to view SNNs not merely as efficient quantization, but as dynamical systems capable of stable transience, offering a physical bridge to the next generation of intelligence.
Locally active memristor-based Chialvo neuron model: bifurcation, multistability, and noise effects.
Rajagopal S, Parastesh F, Pham VT … +2 more , Rajagopal K, Jafari S
Cogn Neurodyn · 2026 Dec · PMID 42212278 · Full text
In this paper, a new memristive Chialvo neuron map is proposed by incorporating a locally active discrete memristor into the original Chialvo neuron model. The introduced memristor exhibits local activity, nonvolatility,...
In this paper, a new memristive Chialvo neuron map is proposed by incorporating a locally active discrete memristor into the original Chialvo neuron model. The introduced memristor exhibits local activity, nonvolatility, and bistability, which fundamentally enrich the neuron dynamics. Equilibrium points and their stability are analyzed as functions of the memristive coupling strength, revealing that the number of equilibrium points increases with increasing memristor strength, indicating enhanced multistability. Comprehensive dynamical analyses based on bifurcation diagrams and largest Lyapunov exponents demonstrate that the proposed model exhibits a wide range of firing behaviors, including periodic, quasiperiodic, and chaotic dynamics. It is shown that increasing the memristive coupling strength generally suppresses chaotic regions, a result further confirmed by spectral entropy analysis. Time-series and phase-space investigations reveal that the memristor can induce qualitative transitions between different firing patterns and modify oscillation periods. In contrast to the original Chialvo model, which mainly exhibits bistability between resting and oscillatory states, the proposed memristive model is capable of exhibiting bistability and multistability between distinct oscillatory attractors, strongly dependent on the initial state of the memristor. Furthermore, the effect of stochastic perturbations is examined, showing that additive noise can induce spiking and bursting dynamics in parameter regions where the noise-free system remains quiescent. As the memristive coupling strength increases, a lower noise intensity is required to induce oscillatory activity.
Reliable resting-state EEG connectivity measures reveal brain network destabilization and cognitive decoupling following prolonged extreme working conditions.
Çelik S, Aydın S
Cogn Neurodyn · 2026 Dec · PMID 42180559 · Full text
This study aimed to characterize alterations in functional brain network organization in miners with approximately ten years of occupational exposure to extreme working conditions and shift work, using connectivity metri...
This study aimed to characterize alterations in functional brain network organization in miners with approximately ten years of occupational exposure to extreme working conditions and shift work, using connectivity metrics derived from resting-state EEG recorded during both eyes-open (EO) and eyes-closed (EC) conditions. Directed Transfer Function (DTF), the imaginary part of Coherence, and the weighted Phase Lag Index were computed from non-overlapping 6-s epochs following two preprocessing pipelines: Independent Component Analysis and Artifact Subspace Reconstruction (ASR). Analyses were conducted for both miners (19 men, mean age 36.52 ± 5.08 years) and matched controls (19 men, mean age 35.42 ± 5.04 years). DTF demonstrated consistently excellent reliability (Intraclass Correlation Coefficients ≥ 0.75) and revealed significant group differences across all frequency bands when combined with ASR, as determined by linear mixed-effects models with false discovery rate correction (pc < 0.05), underscoring its high reproducibility. Specifically, miners exhibited reduced network segregation and integration, reflected by decreases in modularity (Q), global efficiency (GE), local efficiency (LE), clustering coefficient (CC), and transitivity (T) in the delta and theta bands, as well as reduced network resilience (R) at higher frequencies in the EC condition. Within the miner group, higher GE was associated with poorer executive function and slower processing speed, as measured by Trail Making Test subcomponents (0.51 ≤ r ≤ 0.71; 0.0008 ≤ pc ≤ 0.029). In addition, lower-frequency network metrics (CC, LE, T, and R) showed significant negative correlations with verbal recall performance (- 0.70 ≤ r ≤ - 0.54; 0.0009 ≤ pc ≤ 0.039). Collectively, these findings indicate that chronic occupational exposure disrupts the stability and large-scale organization of functional brain networks, resulting in reduced network efficiency and a decoupling between neural connectivity and cognitive performance. From a methodological perspective, the combination of DTF and ASR emerged as the most reliable approach for resting-state EEG connectivity analysis.
Firing behaviors of a neuron with two memristor channels.
Sang Y, Su M, Song X … +2 more , Yang F, Chen J
Cogn Neurodyn · 2026 Dec · PMID 42165010 · Full text
The nonlinear circuit can achieve the functionality and controllability by adding functional electronic components. A nonlinear circuit is considered as a neural circuit, which can construct functional neural circuit by...
The nonlinear circuit can achieve the functionality and controllability by adding functional electronic components. A nonlinear circuit is considered as a neural circuit, which can construct functional neural circuit by adding different functional electronic component branches in neural circuit. This paper proposes a hybrid memristive neural circuit, to explore the regulation of ion channels in neurons by external magnetic fields, and the control of electrical activities in neurons by external electric fields. In this memristive circuit, a magnetic flux-controlled memristor (MFCM) is connected in series with a feedback voltage source to form a branch, which simulates the ion channels of the neuron, and a charge-controlled memristor (CCM) branch is used to capture the external electric fields. The corresponding neuron model is obtained by applying Kirchhoff's theorem and the fundamental theory of circuits. Furthermore, firing behaviors of neuron under external magnetic and electric fields are explored. The analysis indicates that firing behaviors of neuron can be activated by changing intensity of external magnetic and electric field. In addition, an adaptive growth scheme based on energy proportion control is designed for exploring the adaptive regulatory characteristics of ion channels in neurons. The results confirmed that the firing mode of neuron can be regulated by controlling its ion channels. This neuron can also be utilized to construct neural networks for investigating the influence of external electromagnetic fields on their collective behavior.
The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.
Miltiadous A, Ntetska A, Aspiotis V … +7 more , Moustakli E, Tsipouras MG, Tzallas AT, Giannakeas N, Glavas E, Angelidis P, Tzimourta KD
Cogn Neurodyn · 2026 Dec · PMID 42165009 · Full text
Accurate and reproducible electroencephalography (EEG)-based classification of dementia remains a key challenge in computational neurodiagnostics. The open-access AHEPA dataset has become the most commonly used benchmark...
Accurate and reproducible electroencephalography (EEG)-based classification of dementia remains a key challenge in computational neurodiagnostics. The open-access AHEPA dataset has become the most commonly used benchmark for Alzheimer's disease (AD) and Frontotemporal dementia (FTD) classification, yet reported results vary widely due to methodological inconsistencies. This study presents the first systematic and quantitative benchmark review of all published machine learning approaches applied to the AHEPA dataset. Forty-six studies were reviewed and stratified into three validity tiers, with Validity 1 representing the highest methodological rigor and Validity 3 the lowest.According to their evaluation rigor: (1) subject-level validation (e.g., Leave-One-Subject-Out cross-validation, LOSO-CV), (2) subject-level train/test splits, and (3) epoch-level k-fold cross-validation. Performance metrics were normalized across classification problems. The analysis revealed that methodological rigor is inversely correlated with reported accuracy: for AD versus Cognitively Normal controls, mean accuracy decreased from 90.81% overall to 82.11% in Validity-1 studies; for FTD versus controls, accuracy dropped from 86.53% to 75.18%. Linear regression analyses demonstrated that weaker validation protocols were associated with systematic increases of 7-10% points in reported accuracy, explaining more than half of the observed performance variance. Deep and hybrid models reported the highest nominal accuracies, but under proper validation, traditional algorithms performed comparably, indicating that data leakage often drives apparent improvements. The review also highlights the lack of cross-configuration generalization and the urgent need for adaptive, montage-independent methodologies. Overall, this benchmark establishes the first reproducible reference framework for EEG-based dementia classification on the AHEPA dataset, providing quantitative baselines and validity criteria against which all future studies should be evaluated.
Do all roads lead to Rome? The challenges of building brain-like machines.
Liu J
Cogn Neurodyn · 2026 Dec · PMID 42165008 · Full text
Revealing the working mechanisms of the brain and constructing brain-like machines has long been a goal of humanity. However, due to limitations in observational techniques, simultaneous high spatial and high temporal re...
Revealing the working mechanisms of the brain and constructing brain-like machines has long been a goal of humanity. However, due to limitations in observational techniques, simultaneous high spatial and high temporal resolution imaging of the entire brain remains unachievable to date. The product of spatial coverage resolution and temporal resolution appears to be greater than a constant, and the order of magnitude of this constant is currently [Formula: see text]. Given these constraints, the development of brain-like machines has diverged into two distinct paradigms: the top-down approach, which first identifies the functions of macroscopic brain regions, constructs simplified models for each functional module, and then simulates the dynamic behaviors of the entire brain; and the bottom-up approach, which first clarifies the functions of individual neurons, determines network connection patterns, and subsequently infers the brain's global dynamic behaviors through large-scale simulations. Notably, the underlying physical laws governing these two paradigms may be inconsistent. This paper presents a comprehensive discussion addressing this critical issue.
Changes in aperiodic EEG activity following acupuncture stimulation in stroke.
Hu Z, Zhang Z, Xu F … +4 more , Wu S, Wang J, Li G, Yu H
Cogn Neurodyn · 2026 Dec · PMID 42153186 · Full text
Electroencephalography (EEG) enables the direct measurement of stroke-related brain activity, which is crucial for objectively evaluating therapeutic effects of neuromodulatory interventions in stroke. Numerous studies i...
Electroencephalography (EEG) enables the direct measurement of stroke-related brain activity, which is crucial for objectively evaluating therapeutic effects of neuromodulatory interventions in stroke. Numerous studies indicate that EEG spectra comprise a mixture of periodic oscillations and aperiodic background; however, how aperiodic activity, distinct from oscillatory components, characterizes and quantifies external stimulation remains unclear. In this paper, we parameterized the power spectra of EEG recorded from 30 stroke subjects with acupuncture stimulation and investigated the changes of oscillatory and aperiodic brain activity following acupuncture stimulation. For the oscillatory components, we found that acupuncture increased both the power and center frequency of alpha oscillations in the left fronto-parietal cortex, indicating a shift toward faster and more strongly synchronized activity in the lesioned regions. Next, analysis of aperiodic brain activity demonstrated widespread decreases in the aperiodic exponent and offset, most prominently in the ipsilesional hemisphere. These globally and spatially coherent changes suggest that aperiodic dynamics are highly sensitive to acupuncture, reflecting coordinated changes in the background spectral organization. Furthermore, analyses of both periodic and aperiodic features revealed that acupuncture improved interhemispheric symmetry, with particularly pronounced effects observed in alpha center frequency, alpha power, and the aperiodic exponent. Collectively, this study extracted oscillatory and aperiodic spectral features to characterize EEG activity, identified alpha center frequency, alpha power, and the aperiodic exponent as potential biomarkers of acupuncture-induced changes in stroke, and provided evidence for the modulatory effects of peripheral acupuncture stimulation on brain activity.
Mechanisms of taVNS-induced working memory enhancement: a multimodal fNIRS-HRV-MEP study.
Wang M, Dou W, Gong X … +3 more , Gui Y, Lin F, Liang D
Cogn Neurodyn · 2026 Dec · PMID 42153185 · Full text
UNLABELLED: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising neuromodulation technique for enhancing cognitive functions such as working memory (WM), yet its neural mechanisms remain un...
UNLABELLED: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising neuromodulation technique for enhancing cognitive functions such as working memory (WM), yet its neural mechanisms remain unclear. In this study, fifty healthy young adults were randomly assigned to an active taVNS group or a sham group in a single-blind, sham-controlled design. Participants performed semantic and spatial 2-back WM tasks before and after stimulation. Hemodynamic activity was measured by functional near-infrared spectroscopy (fNIRS), autonomic activity was assessed by heart rate variability (HRV), and cortical excitability was indexed by central motor conduction time (CMCT) derived from single-pulse transcranial magnetic stimulation (TMS). Compared to sham, taVNS significantly accelerated reaction times across tasks and increased HRV metrics (HF, SDNN, RMSSD), indicating enhanced parasympathetic regulation. In addition, CMCT was shortened, reflecting improved corticospinal transmission efficiency. fNIRS revealed lateralized activation patterns, with greater hemodynamic engagement in regions corresponding to the left supramarginal gyrus (SMG.L) in semantic tasks and the putative right SMG (SMG.R) in spatial tasks. Mediation analyses suggested that HRV modulated semantic WM performance via SMG.L activation, while spatial WM performance was mediated by CMCT reduction. These findings support a neural resource allocation model in which taVNS facilitates WM through both domain-specific cortical modulation and enhanced efficiency in descending motor pathways, highlighting its potential as a non-invasive intervention to modulate brain-body interactions and enhance cognition. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10465-9.
Robust decoding for MI-EEG: a hybrid transformer network using multi-perspective collaborative attention and dynamic hyperbolic tangent.
Wang M, Gong Z, Li Y … +2 more , Li Y, Zheng Y
Cogn Neurodyn · 2026 Dec · PMID 42153184 · Full text
Deep learning methods have been extensively applied in brain-computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, most existing hybrid architectures often...
Deep learning methods have been extensively applied in brain-computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, most existing hybrid architectures often struggle to effectively eliminate redundant noise in multi-channel signals and lack adaptability to the inherent non-stationarity and distribution drift of EEG signals. This work proposes a novel end-to-end hybrid attention Transformer network (HATNet) for EEG classification. HATNet first employs a convolutional neural network to extract local spatio-temporal features. To overcome the limitations of existing models, it fuses a Collaborative Attention Mechanism for Lightweight Channels, which dynamically recalibrates feature channels through multidimensional pooling strategies, including entropy pooling, to achieve precise spatial noise suppression. Addressing the non-stationary nature of EEG signals, an innovative Dynamic Hyperbolic Tangent module drives the Transformer encoding layer, adapting in real-time to data distribution drifts and significantly enhancing the model's ability to capture individual variations. Furthermore, cross-layer residual fusion pathways deeply integrate global contextual features with raw local spatio-temporal features. To ensure clear scope definition, experiments explicitly distinguish between primary MI tasks and auxiliary motor execution (ME) tasks. HATNet's performance was evaluated on three primary MI benchmark datasets, namely BCIC-IV-2a, BCIC-IV-2b, and the large-scale OpenBMI, as well as one auxiliary ME dataset, HGD. Experimental results demonstrate that HATNet achieves state-of-the-art performance across all analyses. In subject-dependent evaluations, average accuracy rates reached 81.25%, 86.65%, and 69.57% on the three primary MI datasets respectively, and 96.20% on the auxiliary ME dataset. Furthermore, in subject-independent evaluations, it achieved 60.88%, 80.79%, and 76.28% on the MI datasets respectively, alongside 73.95% on the ME dataset. Through multidimensional feature selection and dynamic adaptive modeling, HATNet exhibits superiority and robustness in enhancing both MI and ME decoding performance.
Rosmarinic acid mitigates age-related loss of EEG complexity in temporal cortex: a recurrence quantification analysis.
Uslu S, Kantar D
Cogn Neurodyn · 2026 Dec · PMID 42109930 · Full text
UNLABELLED: Physiological aging is associated with progressive changes in brain function, including neural oscillations. Recurrence Quantification Analysis (RQA) may provide a nuanced perspective on neural dynamics in ag...
UNLABELLED: Physiological aging is associated with progressive changes in brain function, including neural oscillations. Recurrence Quantification Analysis (RQA) may provide a nuanced perspective on neural dynamics in aging. Rosmarinic acid (RA) has shown promise in mitigating age-related neurodegeneration. Its effects on EEG complexity in aging models remain unexplored. This study aimed to investigate age-related changes in RQA metrics and to assess whether RA modulates these alterations in a rodent aging model. Aging was induced in female rats via D-galactose administration. RA was administered to aged model animals. Urethane-induced EEG were obtained and analyzed across delta and theta bands. RQA parameters-determinism (DET), entropy (ENTR), and laminarity (LAM)-were computed for frontal and temporal regions. Aging was associated with a significant increase in DET and LAM, particularly in the temporal cortex, indicating enhanced regularity and persistence of EEG patterns. Concurrently, ENTR values declined, suggesting reduced signal complexity. RA partially reversed these trends, notably decreasing DET and LAM while increasing ENTR values in the temporal cortex. No significant changes were observed in the frontal cortex. This study underscores the utility of RQA in capturing nonlinear EEG alterations associated with aging and highlights RA as a promising compound for preserving cortical dynamics in senescence. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10457-9.
Fundamental questions on closed-loop neuromodulation: a control theory perspective.
Wang M, Zhang L, Liang Z … +1 more , Huang G
Cogn Neurodyn · 2026 Dec · PMID 42109929 · Full text
Closed-loop neuromodulation aims to adjust therapeutic stimulation in real time based on ongoing neural or physiological signals. Despite growing clinical adoption, most implementations rely on heuristic rules rather tha...
Closed-loop neuromodulation aims to adjust therapeutic stimulation in real time based on ongoing neural or physiological signals. Despite growing clinical adoption, most implementations rely on heuristic rules rather than a principled systems-and-control formulation. This paper, motivated by discussions from the Brain Theory Seminar (Shanghai, March 2025), develops such a formulation around seven fundamental questions-mechanism (Q1), plant nature (Q2), state measurement (Q3), actuation (Q4), modeling (Q5), objectives (Q6), and constraints (Q7)-and, for each, provides a knowledge-based review synthesizing current understanding together with a prospective scientific opinion on unresolved issues. Five recurring themes unify the seven questions: (i) nonstationarity as the default operating condition, (ii) structural partial observability and under-actuation, (iii) closed-loop confounding between stimulation and measurement, (iv) the primacy of hard constraints over unconstrained optimization, and (v) the necessity of layered governance separating performance seeking from safety enforcement. We argue that the neural plant is fundamentally different from classical engineered systems in ways that reshape what can be sensed, modeled, actuated, and verified; accordingly, we reframe therapeutic goals from setpoint tracking toward set-based regulation within a therapeutic window, and we treat safety, ethics, and accountability not as external add-ons but as architectural primitives that define the admissible design space. We close with a discussion synthesizing system-level barriers and near-term architectural directions, including bidirectional brain-computer interfaces, hybrid learning-and-control pipelines with independent safety supervision, and digital twins as regulated test harnesses.
Analytical exploration of fractional neural wave structures and their stability characteristics.
Ahmad J, Masood K, Javed H … +1 more , Shah NA
Cogn Neurodyn · 2026 Dec · PMID 42109928 · Full text
The nonlinear fractional soliton neuron model is used in many complicated domains, including fluid mechanics, applied science, neuroscience, nonlinear dynamics, mathematical physics, engineering, biosciences, plasma phys...
The nonlinear fractional soliton neuron model is used in many complicated domains, including fluid mechanics, applied science, neuroscience, nonlinear dynamics, mathematical physics, engineering, biosciences, plasma physics, and geology. It demonstrates how nonlinear waves propagate. This article explores the the fractional nonlinear soliton neuron model, which can play a crucial role in understanding complicated phenomena in neuroscience. This model describes how axons generate and transmit action potentials using a thermodynamic theory of nerve transmission. Signals flowing through the cell membrane (CM) are believed to be single sound pulses, also known as solitons. In this paper we applied the modified Sardar sub-equation method to investigate the exact traveling wave solutions to fractional nonlinear soliton neuron model. In order to discuss the wave patterns of the model, the principal nonlinear equation is transformed to an ordinary differential equation by the usual traveling-wave transformation. The given method provides precise solutions in the form of hyperbolic, trigonometric, and exponential, which results in a well-defined system of solitary wave structures. Consequently, we obtain distinct types of solutions, containing bright solitons, dark solitons, bright-peakon mixtures, peakon and anti-peakon shapes, cuspon and compacton, compacton-kink, kink and anti-kink, dark-bright, bell-shaped solitons, singular periodic, and isolated periodic. The obtained solutions are described in terms of their physical behavior with the help of several visual tools, such as 2D and 3D surface plots, density plots, and contour plots, which have been used to display how the fractional terms reshape the wave profiles, alter their strength, and influence their overall stability. The findings provide a better understanding of the nonlinear wave propagation in neuron-inspired systems and suggest that it may be relevant in any optical media, plasma environment, and computational modeling of wave-based dynamics. Besides these analytical solutions, there is also a modulation instability study, which brings out the fact that minimal perturbation can cause instability of the underlying wave forms. This additional understanding can be used to explain the sensitivity of the model and the circumstances within which wave patterns are stable or start to increase.
Extinction reshapes SWR-coupled reactivation of behavior-defined assembly templates and auditory responses during sleep in the hippocampus and auditory cortex.
Liu T, Xu H
Cogn Neurodyn · 2026 Dec · PMID 42109927 · Full text
UNLABELLED: Sleep supports memory processing, and hippocampal sharp-wave ripples (SWRs) are associated with the re-expression of waking population activity in hippocampal-cortical networks. However, how extinction traini...
UNLABELLED: Sleep supports memory processing, and hippocampal sharp-wave ripples (SWRs) are associated with the re-expression of waking population activity in hippocampal-cortical networks. However, how extinction training alters SWR physiology, SWR-coupled assembly activity, and sensory responsiveness during subsequent sleep remains unclear. Here, we recorded neuronal activity in hippocampal CA1 and auditory cortex (AC) in mice during sleep after overtraining and after extinction in a sound-guided T-maze task. Post-extinction sleep contained more SWRs, but these events showed lower ripple frequency and reduced sharp-wave amplitude. At the single-neuron level, CA1 pyramidal cells and interneurons displayed elevated firing during post-extinction sleep, whereas their SWR-evoked responses were reduced; in contrast, AC neurons showed enhanced SWR-evoked firing. Using assembly templates extracted from post-learned locomotor activity, we found that SWR-coupled expression of behavior-defined CA1 assembly templates was reduced during post-extinction sleep, whereas the corresponding expression of AC templates was enhanced. Additional representational-similarity, shuffle, dropout, and contribution analyses supported an assembly-level interpretation of these effects. In parallel, CA1 sound-suppressed neurons exhibited stronger baseline-corrected auditory responses during post-extinction sleep. Together, these findings indicate that extinction reshapes SWR physiology, redistributes hippocampal-cortical assembly expression, and alters auditory responsiveness during subsequent sleep. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10459-7.
Study of sequential emotional dynamics: a LSDVG approach with reduced electrode configuration.
Farman SA, Kamanoor S, Manimaran P
Cogn Neurodyn · 2026 Dec · PMID 42109926 · Full text
Emotion recognition plays a prominent role in adaptive healthcare and human-computer interaction. Among various techniques, electroencephalogram (EEG) based emotion recognition emerged as a more reliable alternative to t...
Emotion recognition plays a prominent role in adaptive healthcare and human-computer interaction. Among various techniques, electroencephalogram (EEG) based emotion recognition emerged as a more reliable alternative to traditional methods such as facial expression or voice tone analysis. This work attempts to study how the history of emotional stimuli shows an impact on a person's current emotional response, which is relatively less investigated aspect of EEG based emotion recognition. Initially, local standard deviation (LSD) is used for preprocessing EEG data, to reduce the data size while preserving crucial temporal signal fluctuations. The preprocessed EEG data is transformed into a visibility graph (VG), from which five topological properties such as modularity, number of communities, density, average degree, and differential entropy are calculated. The properties extracted are used to perform three binary classification tasks: positive vs. negative, negative vs. neutral, and positive vs. neutral, across all possible sequential combinations of the emotional video trail. The classification results indicated that highest accuracy is achieved when the emotional sequences are new, whereas repetition either in the video source or in the combination of emotional stimuli made the subjects emotionally familiar and consequently led to less distinct emotional responses. This approach used only 12 EEG channels to achieve high accuracy, when compared to earlier studies that generally required 18 or more channels. Thus, the LSDVG-based approach can be seen as a computationally efficient approach for emotion recognition and also helps in understanding the influence of past emotional history on the present emotional response of the brain.
Acas-eer: attention-based CNN architecture search with spatial-spectral feature for emotional EEG recognition.
Qiao Y, Zhao Q
Cogn Neurodyn · 2026 Dec · PMID 42109925 · Full text
Electroencephalogram (EEG)-based analysis of emotional states is important for health assessment, emotion monitoring, and treatment support. Accurate EEG emotion recognition depends on both rich emotional features and we...
Electroencephalogram (EEG)-based analysis of emotional states is important for health assessment, emotion monitoring, and treatment support. Accurate EEG emotion recognition depends on both rich emotional features and well-designed recognition models. While deep learning can automatically extract features and classify data effectively, it often fails to produce diverse emotional EEG features, and manually designing network structures is time-consuming. Neural architecture search (NAS) helps find the best network design and reduces manual effort. In this work, we propose an attention-based convolutional neural network (CNN) architecture search method using spatial-spectral features for EEG emotion recognition, called ACAS-EER. It is optimized to better match emotional EEG characteristics. Firstly, differential entropy (DE) and power spectral density (PSD) were combined to create EEG feature maps, providing more spatial and spectral information. Secondly, a search space with four lightweight attention modules was designed. The final architecture, found automatically, includes convolution, pooling, and multiple attention modules. Also, an operation-level Dropout method was used to avoid poor performance caused by too many parameterless operations. On the DEAP dataset, ACAS-EER achieved high average accuracy and F1 scores (97.40% and 97.33% for valence; 97.68% and 97.35% for arousal). It performed better than most hand-designed deep learning models and other NAS models.
Classification of consciousness disorders based on graph convolution and attention mechanism.
Yin N, Zhang J, Wang H … +1 more , Zhao J
Cogn Neurodyn · 2026 Dec · PMID 42028461 · Full text
This study develops an interpretable, task-adaptive EEG diagnostic framework to overcome the subjectivity and time burden of behavioral scales like the CRS-R. The model is designed to learn task-relevant inter-electrode...
This study develops an interpretable, task-adaptive EEG diagnostic framework to overcome the subjectivity and time burden of behavioral scales like the CRS-R. The model is designed to learn task-relevant inter-electrode connectivity directly from data via a trainable adjacency matrix within a graph-convolutional architecture, making it robust to individual and state-related variability. It also integrates a channel-time dual-dimensional Efficient Channel Attention mechanism to jointly model spatial and temporal dependencies. Interpretability is emphasized by extracting model-derived channel saliency to identify a compact set of clinically informative electrodes and by testing whether these reduced-channel subsets can sustain high diagnostic performance. Clinical applicability is prioritized by validating stability with cross-validation and by targeting bedside deployment through channel reduction and streamlined decoders, while laying groundwork for physiological interpretation and multi-center, multimodal validation. We propose GCENet, which uses three stacked GCN layers with a learnable adjacency matrix and ECA modules to dynamically weight channel and temporal features. Data comprised 119 EEG segments recorded with a 20-channel system; performance was assessed using ten-fold cross-validation. GCN-ECA achieved mean accuracy of 87.12% and AUC of 92.76%, outperforming baselines. The learned connectivity emphasized frontal and occipital channels, supporting an interpretable channel-reduction strategy. This attention-enhanced GCN offers an objective, scalable alternative to behavioral assessment and a practical path toward reduced-channel, bedside DOC monitoring and broader clinical validation.
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