Cogn Neurodyn [JOURNAL]
Sun
200 papers
Dynamic temporal patterns of DMN connectivity in epilepsy using hidden (semi-) Markov models.
Amoiridou D, Kakkos I, Gkiatis K … +4 more , Miloulis ST, Vezakis I, Garganis K, Matsopoulos GK
Cogn Neurodyn · 2026 Dec · PMID 41245997 · Full text
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seiz...
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seizure propagation. In this study, we investigate the temporal patterns of DMN connectivity in epilepsy patients compared to healthy controls using data-driven models of dynamic functional connectivity (dFC). Specifically, we employ one Hidden Markov Model (HMM) and two Hidden Semi-Markov Models (HSMMs) with Gamma and Poisson sojourn distributions to capture latent brain state transitions, as well as hidden connectivity states and their temporal properties. Dynamic metrics (i.e., fractional occupancy, switching rate, and mean lifetime) were derived for each subject, revealing prolonged dwell times in low-connectivity states and reduced flexibility in state transitions, particularly in low-connectivity DMN states. HSMMs, especially the Gamma variant, demonstrated superior sensitivity in capturing these alterations compared to the standard HMM, highlighting the importance of flexible sojourn modeling in dynamic functional connectivity analysis. Additionally, group-specific transition patterns suggested disrupted temporal progression of DMN state transitions. Our findings highlight the potential of HSMMs in capturing alterations in functional brain states and provide new insights into the dynamic reorganization of the DMN in epilepsy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10382-3.
Synaptic summation shapes information transfer in GABA-glutamate co-transmission.
Krubitski B, Ceballos C, Roachford T … +1 more , Pena RFO
Cogn Neurodyn · 2026 Dec · PMID 41245996 · Full text
UNLABELLED: Co-transmission, the release of multiple neurotransmitters from a single neuron, is an increasingly recognized phenomenon in the nervous system. A particularly interesting combination of neurotransmitters exh...
UNLABELLED: Co-transmission, the release of multiple neurotransmitters from a single neuron, is an increasingly recognized phenomenon in the nervous system. A particularly interesting combination of neurotransmitters exhibiting co-transmission is glutamate and GABA, which, when co-released from neurons, demonstrate complex biphasic activity patterns that vary depending on the time or amplitude differences from the excitatory (AMPA) or inhibitory (GABA) signals. Naively, the outcome signal produced by these differences can be functionally interpreted as simple mechanisms that only add or remove spikes by excitation or inhibition. However, the complex interaction of multiple time-scales and amplitudes may deliver a more complex temporal coding, which is experimentally difficult to access and interpret. In this work, we employ an extensive computational approach to distinguish these postsynaptic co-transmission patterns and how they interact with dendritic filtering and ionic currents. We specifically focus on modeling the summation patterns and their flexible dynamics that arise from the many combinations of temporal and amplitude co-transmission differences. Our results indicate a number of summation patterns that excite, inhibit, and act transiently, which have been previously attributed to the interplay between the intrinsic active and passive electrical properties of the postsynaptic dendritic membrane. Our computational framework provides an insight into the complex interplay that arises between co-transmission and dendritic filtering, allowing for a mechanistic understanding underlying the integration and processing of co-transmitted signals in neural circuits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10383-2.
Emergence of functionally differentiated structures via mutual information minimization in recurrent neural networks.
Tomoda Y, Tsuda I, Yamaguti Y
Cogn Neurodyn · 2026 Dec · PMID 41245995 · Full text
Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks w...
Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task and a chaotic signal separation task involving Lorenz and Rössler time series. Analysis of network performance, correlation patterns, and weight matrices reveals that mutual information minimization yields high task performance alongside clear functional modularity and moderate structural modularity. Importantly, our results show that functional differentiation, which is measured through correlation structures, emerges earlier than structural modularity defined by synaptic weights. This suggests that functional specialization precedes and probably drives structural reorganization within developing neural networks. Our findings provide new insights into how information-theoretic principles may govern the emergence of specialized functions and modular structures during artificial and biological brain development.
Critical behaviors of modular networks under local excitatory-inhibitory fluctuations.
Yang C, Liu Z, Luan G … +1 more , Ren J
Cogn Neurodyn · 2026 Dec · PMID 41245994 · Full text
Numerous physiological observations have shown that the brain operates at the edge of a critical state between order and disorder. Meanwhile, brain structures at different scales, from cortical columns to the entire brai...
Numerous physiological observations have shown that the brain operates at the edge of a critical state between order and disorder. Meanwhile, brain structures at different scales, from cortical columns to the entire brain, are organized in a modular manner. However, whether modular brain networks represent the optimized structure shaped for criticality and in what ways, have not been fully answered. In this study, a modular network with dense intra-module links but sparse inter-module links is established, and the behavior of each neuron is governed by the Kinouchi-Copelli model. Moreover, randomized surrogate networks with identical degree distribution are introduced to illustrate the significance of modular structures for criticality. Results suggest that the modular network requires fewer synaptic resources and lower firing costs to achieve criticality. More importantly, smaller avalanches indicate that the modular structures can enhance network resilience, facilitating rapid recovery from perturbations. Furthermore, by testing the sensitivity of the network state to local excitatory-inhibitory fluctuations, it is found that the efficiency of excitatory and inhibitory regulation is closely related to the 2-level excitatory input density. In addition, inhibitory regulation targeting modules with larger maximum real eigenvalues can more effectively suppress hyperexcitatory activities to achieve balance. When local excitation is greatly enhanced, even if the modular network is adjusted to the critical state, the size-to-duration ratio of module-level avalanches can effectively capture abnormalities. The properties also manifest in clinical recordings from patients with temporal lobe epilepsy, which may provide a promising method for epileptogenic zone localization.
Research on the classification of EEG signals for dementia and its interpretability using the GWOCS agorithm.
Wang R, Xu H, Ma Y … +1 more , Che Y
Cogn Neurodyn · 2026 Dec · PMID 41221324 · Full text
Alzheimer's disease (AD) and frontotemporal dementia (FTD) have insidious, similar and ambiguous clinical symptoms, which make their diagnosis difficult. Currently, in the field of EEG signal analysis, there are relative...
Alzheimer's disease (AD) and frontotemporal dementia (FTD) have insidious, similar and ambiguous clinical symptoms, which make their diagnosis difficult. Currently, in the field of EEG signal analysis, there are relatively few studies on the interpretability analysis of feature selection using intelligent optimization algorithms. To analyze the EEG signals of AD and FTD patients more comprehensively, first, 16 features in three dimensions of entropy, time-frequency domain and SODP were extracted in this paper. Secondly, Pearson correlation analysis, importance ranking and SHAP interpretability analysis methods were adopted to select SE, SW, ZCR, STA, CTM2 and CTM5 as the best discriminative features, and the Relief algorithm was used for fusion and dimension reduction based on weights. Thirdly, GWOCS was used for channel screening to determine the optimal channel combination of Fz, F7, Fp1, Fp2, F3, T3, P4 and C3, achieving the three-classification identification of the two patient groups and the normal control group, with the classification accuracy reaching 89.35[Formula: see text] and 81.12[Formula: see text] in cross-validation and LOSO validation, respectively. Finally, the SHAP method was used to prove that for the diagnosis of dementia, the prefrontal and temporal lobe brain regions play a decisive role, verifying the effectiveness of this framework in rapid channel selection and improving the efficiency of disease detection.
A multimodal deep learning framework for functional brain network classification in rs-fMRI.
Robinson B, Reuther W, Leggio O … +4 more , Cediel EG, Jeyabose A, Kazemi MH, Boerwinkle VL
Cogn Neurodyn · 2025 Dec · PMID 41220406 · Full text
To automate the classification of functional brain networks in epilepsy patients using resting-state functional magnetic resonance imaging (rs-fMRI). The study introduces a deep learning framework that leverages spatial...
To automate the classification of functional brain networks in epilepsy patients using resting-state functional magnetic resonance imaging (rs-fMRI). The study introduces a deep learning framework that leverages spatial and temporal features to classify Independent Component Analysis (ICA)-derived networks into 11 functionally distinct classes, including seizure onset zone (SoZ), resting-state networks (RSNs), and artifact/noise. A hybrid deep learning architecture was developed combining a 3D Convolutional Neural Network (3D-CNN) to extract spatial features (SF) and a Long Short-Term Memory (LSTM) network to capture temporal dynamics from time-domain (TS) and frequency-domain (FS) signals. These multi-domain features were concatenated and classified into 11 distinct ICA component types. An ablation study assessed the individual and combined contributions of spatial, temporal, and spectral features. Additionally, expert neurologists independently rated four representative cases to qualitatively validate the model's interpretability and clinical relevance. The baseline 3D CNN (SF) model achieved an overall accuracy of 69% with a sensitivity of 0.52 and a ROC AUC of 0.76. Incorporating frequency-domain signals (SF + FS) enhanced sensitivity to 0.54 and improved the ROC AUC to 0.78 while maintaining a similar accuracy. Combining both time-domain and frequency-domain signals (SF + TS + FS) yielded the highest accuracy at 70%. At the class level, the Noise class consistently demonstrated robust performance (up to 0.94), whereas the temporal lobe network class Temporal class exhibited lower scores (0.14-0.24) across all configurations. Our results demonstrate that this data-driven framework can effectively automate the classification of rs-fMRI-derived functional brain networks including SoZ thereby reducing subjectivity and workload in clinical review. The inclusion of spatial, temporal, and spectral information enables a richer and more nuanced classification that supports downstream applications in epilepsy surgical planning.
Tunable synchronization control of coupled neural dual-capacitance circuits via switchable components.
Liu Z, Huang S, Chai Y … +2 more , Huang G, Zhu R
Cogn Neurodyn · 2025 Dec · PMID 41215983 · Full text
Understanding how different electrical components influence neuronal synchronization is essential for advancing neural circuit dynamics and therapeutic interventions. However, while previous studies have examined individ...
Understanding how different electrical components influence neuronal synchronization is essential for advancing neural circuit dynamics and therapeutic interventions. However, while previous studies have examined individual components separately, comprehensive comparative analyses of their integrated effects in coupled systems remain limited. This study investigates synchronization dynamics in coupled dual-capacitance neuronal models incorporated with three distinct electrical components: memristor (M), inductive coil (L), and Josephson junction (JJ). The neuronal models are driven by Bessel function-modulated external stimuli to generate rich dynamical behaviors. Using a switchable circuit design, we systematically analyze synchronization characteristics across varying coupling strengths, external stimulation parameters, and noise interference levels. Our results reveal distinct synchronization properties for each configuration: the L model demonstrates high sensitivity to frequency variations, the JJ model exhibits robust synchronization within specific parameter ranges, while the M model shows superior resilience against noise interference. These findings provide insights into component-specific contributions to neuronal synchronization and offer potential applications for neural network design and synchronization-based therapeutic approaches.
The acoustic features of sincere joyful, sad, and fearful human non-verbal vocalizations and its effect on the emotional valence of cat's meowing.
Portnova GV, Liaukovich K, Sharaev M
Cogn Neurodyn · 2025 Dec · PMID 41215982 · Full text
UNLABELLED: The perception of emotional non-verbal vocalizations relies on a complex interplay of specific acoustic features that facilitate the identification of valence and enable appropriate behavioral responses. Here...
UNLABELLED: The perception of emotional non-verbal vocalizations relies on a complex interplay of specific acoustic features that facilitate the identification of valence and enable appropriate behavioral responses. Here, we analyzed over 3,000 videos containing laughter, screams, and cries, selecting 664 highly recognizable and sincere sounds for further study. We computed both linear and nonlinear acoustic parameters, including spectral and temporal features, and a panel of experts evaluated each sound on scales of joy, sadness, fear, and sincerity. Joyful vocalizations were characterized by higher fractal dimensions (FD) and envelope mean frequency (EMF), while sad sounds were distinguished by loudness and reduced acoustic variability. Fearful vocalizations were identified by their minimal and maximal loudness levels and elevated power spectral density (PSD) in the 1-2 kHz range. Sincerity in non-verbal sounds correlated with nonlinear features and PSD in the 0.5-1 kHz range. Utilizing these acoustic parameters, we modified neutral cat meows to incorporate features of joyful, fearful, and sad emotional sounds. These modifications influenced human emotional perception of the meows, revealing an anthropocentric bias in emotional interpretation. Our findings suggest that the emotional perception of cat vocalizations is shaped by human-specific acoustic cues and modulated by the listener's well-being and mood. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10370-7.
A machine learning-based EEG signal analysis framework to enhance emotional state detection.
Sobhani MAA, Srestha SD, Shomoy MMH … +2 more , Reza AW, Siddique N
Cogn Neurodyn · 2025 Dec · PMID 41215981 · Full text
This study aims to present a machine learning-based approach for detecting emotional states from Electroencephalogram (EEG) signals by utilizing multiple machine learning models with various parameter settings to achieve...
This study aims to present a machine learning-based approach for detecting emotional states from Electroencephalogram (EEG) signals by utilizing multiple machine learning models with various parameter settings to achieve the best outcomes. Nine machine learning models, which are Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP), and 1D Convolutional Neural Network (1D CNN), are employed in this study. A dataset consisting of EEG signals from 300 patients is employed to conduct the experiments. Additionally, multiple synthetic datasets of 20,000 data points are generated using Generative Adversarial Network (GAN), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). Both the real and synthetic datasets are utilized for training, testing, and validating the models. By comparing the performance of the models, it is determined that across 5 different datasets (Original, Original + GAN, Original + SMOTE, Original + ADASYN, Original + GAN + SMOTE + ADASYN), the MLP model achieves the highest accuracy and efficiency. They demonstrated a testing accuracy of 98.8% and a latency ranging from only 1.8ms - 4.8ms. The use of synthetic data in machine learning and deep learning models shortens the process and enhances accuracy. The results of this study are promising and hold potential benefits for physicians and healthcare professionals.
Cross-modal alignment and fusion of EEG-visual based on mixed attention mechanism for emotion recognition.
Li L, Chen W
Cogn Neurodyn · 2025 Dec · PMID 41215980 · Full text
UNLABELLED: Changes in human emotions are often accompanied by changes in multiple physiological or external stimuli. Fusing these multi-modal information can improve the accuracy of emotion recognition. However, since c...
UNLABELLED: Changes in human emotions are often accompanied by changes in multiple physiological or external stimuli. Fusing these multi-modal information can improve the accuracy of emotion recognition. However, since current multi-modal emotion recognition algorithms do not consider modal synchronization, the resulting misalignment of information affects emotion recognition accuracy. To address these issues, this paper proposes an Electroencephalogram (EEG)-visual cross-modal alignment and fusion model for emotion classification (CMAF) based on a hybrid attention mechanism. Differential entropy (DE) features of five frequency bands of EEG signals and 10 color features of visual modalities are utilized for cross-modal emotion recognition. The cross-modal alignment module extracts key information by a multi-head attention mechanism, and improves the similarity of two modes under the constraint of loss function. A cross-attention module is designed to use visual modalities to guide feature extraction of EEG signals and establish correlations between two modalities for modal fusion. Support vector machine (SVM) is used to classify the features extracted from different emotional states in SEED dataset. Experimental results show that fusing high-frequency EEG and video features significantly improves recognition accuracy, with the Gamma-visual fusion achieving an average accuracy of 96.49%. To further evaluate the model's generalization capability, we introduced the SEED-IV dataset and conducted assessments on two datasets under both subject-related and subject-independent settings. The results demonstrate that the model consistently maintains robust performance across diverse data sources, highlighting its robustness and generalization potential. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10372-5.
GSANet: research on EEG decoding based on graph attention and self attention in auditory attention detection.
Dong Y, Dai R, Xie T … +2 more , Xu K, Huang L
Cogn Neurodyn · 2025 Dec · PMID 41215979 · Full text
Humans demonstrate the ability to focus auditory attention in noisy environments, enabling them to concentrate on a specific speaker at a cocktail party. Neuroscientific research has shown that auditory attention itself...
Humans demonstrate the ability to focus auditory attention in noisy environments, enabling them to concentrate on a specific speaker at a cocktail party. Neuroscientific research has shown that auditory attention itself is a dynamic brain activity that evolves over time, which has inspired studies on electroencephalography (EEG)-based auditory attention detection (AAD). This paper proposes a neural attention mechanism model named GSANet, which employs a self-attention mechanism to model the temporal dynamics of EEG signals while dynamically assigning weights to EEG channels through a graph attention mechanism. In brief, GSANet simulates the neural attention mechanisms of the human brain to extract discriminative representations from EEG signals for training high-performance classifiers. We conducted experiments on two public datasets, KUL and DTU, achieving overall decoding accuracies of 94.5% and 79.2%, respectively, under a 1-second decision window, significantly outperforming baseline models across all comparative conditions. The code of our proposed method will be available at: https://github.com/dalin6666/GSANet.
Emotion recognition using spatially unidimensional self-attention with fusion feature of brain effective connectivity network and spectral power.
Jiang T, Wang H, Li Q … +1 more , Xu X
Cogn Neurodyn · 2025 Dec · PMID 41199759 · Full text
Electroencephalogram(EEG)-based emotion recognition is crucial for advancing human-computer interaction (HCI), and brain network features have become a key research focus. While existing methods often concatenate brain n...
Electroencephalogram(EEG)-based emotion recognition is crucial for advancing human-computer interaction (HCI), and brain network features have become a key research focus. While existing methods often concatenate brain network features with traditional single-channel features to enhance recognition performance, this direct concatenation undermines the spatial information of brain networks and hinders effective application of deep learning. In this work, we propose a novel feature fusion strategy that effectively combines two-dimensional brain effective connectivity (BEC) network features with one-dimensional spectral power features while preserving spatial information. To leverage the spatial topological properties of brain networks and the one-dimensional correlations in fused features, we further introduce a Dual-channel 1D-CNN based on Spatially Unidimensional Self-Attention (SAD-1D-CNN), designed to extract discriminative features by capturing spatial correlations within the combined data. Results show 90.61% accuracy on SEED and 82.13% on SEED-IV (2.68% higher than state-of-the-art). Comprehensive tests confirm the superiority of our fusion strategy and SAD-1D-CNN in emotion recognition. Parameter visualization reveals the attention module's ability to automatically focus on emotion-related core brain regions, and ablation experiments validate the necessity of each network module. These findings offer new perspectives for advancing emotion recognition research.
A dual-branch neural network and attention mechanism for decoding EEG-based motor imagery.
Wang Y, Yu H, Zhao X … +3 more , Yin X, Li H, Wang C
Cogn Neurodyn · 2025 Dec · PMID 41199758 · Full text
Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for...
Motor imagery (MI) is a fundamental paradigm in brain-computer interfaces (BCIs), extensively employed to assist individuals with disabilities to operate external devices. Accurate decoding of MI signals is essential for effective interaction. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. To address this issue, we propose an innovative Dual-Branch Multi-Attention Temporal Convolutional Network (DBMATCN) to improve the performance of MI-EEG signal classification. First, the dual-branch structure extracts rich spatial-temporal features. Then, the channel attention enhances local channel feature extraction and calibrate feature mapping. Next, by combining a sliding window technique and multi-head locality self-attention improves the feature representation of MI-EEG signals by emphasizing the most relevant features. Finally, the temporal convolution fusion network decoding module is used to extensively capture comprehensive temporal features from MI data and carry out the classification task. DBMATCN achieves average accuracies of 88.08%, 96.83%, and 89.71% in inter-session validation on the BCI-IV-2a, HGD, and BCI-IV-2b datasets, respectively. In cross-validation, the model reaches an accuracy of 85.14%, and in the subject-independent scenario, it attains 71.78%. DBMATCN outperforms all baseline models in these cases. These results suggest that our model is effective in decoding MI signals.
SFT-HN: a novel spatial-frequency-temporal hybrid network for EEG-based emotion recognition.
Zhu L, Ding Y, Hung A … +2 more , Tan X, Zhang J
Cogn Neurodyn · 2025 Dec · PMID 41199757 · Full text
Electroencephalograph (EEG) emotion recognition is a key task in the brain-computer interface(BCI) field. A mounting quantity of studies have shown that deep learning methods for emotion recognition exhibit superior perf...
Electroencephalograph (EEG) emotion recognition is a key task in the brain-computer interface(BCI) field. A mounting quantity of studies have shown that deep learning methods for emotion recognition exhibit superior performance compared to traditional techniques. However, it is still challenging to fuse the EEG's Spatial, Frequency and Temporal information, as well as how to make full use of discriminative local patterns among the features for different emotions. To address these issues, a novel hybrid model called Spatial-Frequency-Temporal Hybrid Network(SFT-HN) is proposed. This model includes three Spatial Frequency Residual Modules (SFRM) and an attention-based Bidirectional Long Short-Term Memory (ATBI-LSTM). The former module extracts spatial-frequency features, while the latter learns temporal contexts. SFT-HN is trained to seize the complementarity among the spatial-frequency-temporal information and adaptively explore discriminative local patterns. Specifically, 4D representations are created from raw EEG signals to preserve spatial, frequency, and temporal information. The SFRM module then adopts split-convert-merge techniques, residual and attention mechanisms to enhance its spatial-frequency feature extraction ability for each input 4D representation tensor time slice. Moreover, an attention-enhanced mechanism is incorporated into a bidirectional LSTM module to capture the crucial temporal dependencies among the extracted features, thereby enhancing the discriminative power of the EEG features. The proposed method attains average accuracies of 97.61% and 97.57% for arousal-based and valence-based classification on the DEAP dataset, respectively. On SEED dataset, the method achieves average accuracy of 97.44%. Furthermore, we validate the robust generalization of our proposed model on a novel dataset, FACED, achieving an average accuracy of 96.24%. The model code is available at: https://github.com/AllGGI/SFT-HN-model.
Generative motor imagery dynamic networks: EEG-controlled grasping via individualized model training.
Wu X, Long D, Yang J
Cogn Neurodyn · 2025 Dec · PMID 41199756 · Full text
Improving the accuracy of non-invasive brain-computer interface (BCI) and promoting their daily use can be achieved by developing an individualized model training framework, where individual training means that the model...
Improving the accuracy of non-invasive brain-computer interface (BCI) and promoting their daily use can be achieved by developing an individualized model training framework, where individual training means that the model is based on small-sample learning from individual data. In the process of data augmentation through synthetic data, the criteria for data generation needs to be further specified according to the requirements. Therefore, in this study, the proposed BCI model utilizes dynamic networks to describe electroencephalogram (EEG) activity during the motor imagery (MI) task, innovatively generates individualized dynamic networks from individual data, and ultimately achieves EEG-controlled grasping through model training. Specifically, this study involves the EEG signals of the right-hand grasping movements of eight subjects and proposes using morphological pattern spectrum (MPS) to encode EEG potentials during MI processes. The MI condition representation was achieved by combining the dynamic networks with MPS encoding, and more dynamic network EEG encoding samples were synthesized through generative adversarial network (GAN) or variational autoencoder (VAE). The AUCs based on the long short-term memory (LSTM) architecture for generating and classifying can be improved by 0.003-0.07. The optimal BCI model based on the Wasserstein GAN and Granger causality (GC) dynamic network encoded by MPS achieved a mean true/false positive rate (TPR/FPR) of 90.0%/0.0%, far better than the 52.9%/4.4% achieved without individualized modeling. Moreover, the BCI establishment of handling multi-task and complex command outputs further demonstrates the reliability of MPS encoding of the GC dynamic network in BCI modeling. The advantage of this "generative-individual" approach is that it not only reduces the sample size requirement while ensuring accuracy but also avoids building models that are applicable to all individuals, which leads to difficult convergence.
Discrimination between focal epilepsy and generalized epilepsy based on the spatial patterns of EEG network.
Chen C, Liang Y, Zhou G … +8 more , Xu S, Li C, Zhou J, Luo L, Yao D, Xu P, Li F, Yu L
Cogn Neurodyn · 2025 Dec · PMID 41199755 · Full text
Temporal lobe epilepsy (TLE), the most common form of focal epilepsy, and generalized epilepsy (GE) are two major clinical subtypes frequently encountered in clinical practice. However, due to limited understanding of th...
Temporal lobe epilepsy (TLE), the most common form of focal epilepsy, and generalized epilepsy (GE) are two major clinical subtypes frequently encountered in clinical practice. However, due to limited understanding of their underlying pathophysiological mechanisms, distinguishing between them based solely on clinical features and conventional electroencephalographic (EEG) characteristics remains challenging. In this study, EEG was employed to investigate differences in both local and global brain activity patterns between TLE and GE across multiple frequency bands-delta, theta, alpha, beta, and gamma. Distinct rhythmic and regional patterns were identified for each subtype. Specifically, we first examined changes in local brain activity, measured as relative power spectral density. TLE was characterized by increased low-frequency (i.e., delta) neuronal synchronization, predominantly localized within the temporal and parietal lobes. In contrast, GE exhibited elevated high-frequency (i.e., beta) activity distributed across a broader range of cortical regions. With regard to global brain activity, assessed through functional connectivity, TLE showed enhanced short-range connections primarily involving the temporal lobe and adjacent areas, particularly within low-frequency bands (i.e., delta and theta). Conversely, GE demonstrated increased long-range connectivity across widespread distant brain regions, especially at higher frequency bands (i.e., alpha and beta). Based on these distinguishing features, we further conducted a classification analysis to differentiate between TLE and GE, achieving an accuracy of 82.98% when combining local and global activity measures. These findings may help elucidate the distinct pathophysiological mechanisms underlying TLE and GE, potentially offering objective biomarkers for improved diagnosis and targeted treatment strategies.
Dynamic brain connectivity: linking inter-group difference and individual susceptibility changes in visually induced motion sickness susceptibility.
Wang Y, Wei Y, So RHY … +2 more , Okazaki YO, Kitajo K
Cogn Neurodyn · 2025 Dec · PMID 41199754 · Full text
UNLABELLED: Visually induced motion sickness (VIMS) is a prevalent discomfort experienced in virtual environments, and individual susceptibility to VIMS can change with training or experience. Currently, the neurological...
UNLABELLED: Visually induced motion sickness (VIMS) is a prevalent discomfort experienced in virtual environments, and individual susceptibility to VIMS can change with training or experience. Currently, the neurological activities that respond to susceptibility changes remain unclear. This study identified dynamic brain connectivity that consistently responded to inter-group susceptibility differences as well as individual susceptibility changes. Participants with varying susceptibility to VIMS underwent adaptation training, which involved repeated exposure to roll rotation stimulation for 7-10 days. VIMS susceptibility and Theta-band EEG phase synchronization were measured before and after the adaptation training. The results revealed that the inter-hemispheric connectivity in temporal-parietal regions not only significantly differed between the susceptible and resistant groups, but also increased with individual resistance enhancement. The strength of this connectivity was negatively correlated to individual level of VIMS symptoms. Machine learning models based on whole brain connection patterns effectively identified susceptible individuals and tracked changes in susceptibility following training. All effects were also observed in untrained stimulus type, indicating the robustness of the connectivity indicators. These findings underscore the importance of inter-hemispheric coordination in VIMS and highlight the potential of EEG phase synchronization as a tool for testing and monitoring individual susceptibility to VIMS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10354-7.
Impact of stimulus presentation speed in a visual ERP-based BCI under RSVP.
Fernández-Rodríguez Á, Velasco-Álvarez F, Vizcaíno-Martín FJ … +1 more , Ron-Angevin R
Cogn Neurodyn · 2025 Dec · PMID 41179694 · Full text
Rapid serial visual presentation (RSVP) is one of the most effective gaze-independent paradigms for event-related potential (ERP)-based brain-computer interfaces (BCIs), particularly for individuals with limited muscle a...
Rapid serial visual presentation (RSVP) is one of the most effective gaze-independent paradigms for event-related potential (ERP)-based brain-computer interfaces (BCIs), particularly for individuals with limited muscle and eye movement control. The speed of visual stimulus presentation is a critical factor influencing system performance and warrants thorough investigation. This study evaluates the impact of different stimulus presentation speeds on the performance of an ERP-BCI used for pictogram selection under RSVP. Thirteen participants tested the ERP-BCI across three experimental conditions, each with a different stimulus onset asynchrony (SOA): 80 ms (C080), 160 ms (C160), and 320 ms (C320). In addition to performance metrics such as accuracy, information transfer rate (ITR), and pictograms per minute (PPM), a subjective evaluation of the user experience was conducted for each condition. The results indicate that C160 outperformed both C080 and C320 across all performance metrics, achieving an ITR of 26.49 bit/min (81.28% accuracy in 4.8 s). Subjective evaluations also revealed a preference for C160 and C320 over C080. Therefore, among the SOAs evaluated, 160 ms appears to be the most suitable for enhancing system usability. These findings underscore the crucial role of stimulus presentation speed in the usability of ERP-BCIs for pictogram selection under RSVP, emphasizing its importance in future gaze-independent ERP-BCI designs for communication purposes.
The influence of different visual eccentricity on SSVEPs elicited by ultra-low frequency visual stimulation in the lower peripheral visual field.
Li X, Ji X, Wang Y … +1 more , Chen X
Cogn Neurodyn · 2025 Dec · PMID 41169537 · Full text
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely explored due to their high information transfer rate (ITR) and minimal training requirements. Traditional SSVEP-based B...
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely explored due to their high information transfer rate (ITR) and minimal training requirements. Traditional SSVEP-based BCIs typically use low- and medium-frequency visual stimuli from the central visual field to induce SSVEPs, but these can easily lead to visual fatigue. In order to improve system's comfort, some studies have attempted to use visual stimuli from the peripheral visual field to elicit SSVEPs. However, few studies have investigated the effects of different visual eccentricities on induced SSVEPs. In this study, we used ultra-low frequency (i.e., 2.00-3.32 Hz) visual stimulation in the lower peripheral visual field to induce SSVEPs. Furthermore, we further explored the effects of different visual eccentricities (i.e., 2.1°, 3.1°, and 4.1°) on induced SSVEPs. Experimental results obtained from twelve participants revealed that all three eccentricity conditions were capable of eliciting SSVEP responses. Moreover, SSVEP amplitude gradually decreased as eccentricity increased. These results provide new parametric references for optimizing the spatial layout of visual stimuli in peripheral SSVEP-based BCI systems.
New insights into multistability and complex resonances driven by subthreshold periodic signals in a neuronal model.
Megchun AF, Padilla-Longoria P, Espinal-Enríquez J … +2 more , Santos GJE, Bernal-Jaquez R
Cogn Neurodyn · 2025 Dec · PMID 41141241 · Full text
Understanding how neurons respond to weak external signals is crucial for accurate signal transmission and processing in both individual nerve cells and interconnected neuronal networks. One mechanism for the detection o...
Understanding how neurons respond to weak external signals is crucial for accurate signal transmission and processing in both individual nerve cells and interconnected neuronal networks. One mechanism for the detection of these responses is through resonances. In this paper, we numerically investigate the firing patterns induced in a silent Huber-Braun neuron by a sinusoidal external force. We observe complex resonance patterns, including a sequence of frequency-locking exhibited in a Devil's Staircase structure. Furthermore, we also explore the emergence of multistability induced by the nonlinear resonance. This multistability manifests as the coexistence of three attractors, such as periodic spiking, chaotic spiking, and subthreshold oscillations. The dynamical behaviors are comprehensively analyzed using time series, bifurcation diagrams, phase portraits, and the basin of attraction. In addition, we compute the maximum Lyapunov exponent to verify chaotic regimes, and estimate the fractal dimension of basin boundaries using the uncertainty exponent. We also analyze the energy consumption of resonance-induced firing patterns and coexisting attractors. The results presented in this paper have important implications for understanding the detection of subthreshold signals and the encoding of stimulus information within a neuron's firing patterns.
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