Cognitive Neurodynamics最新文献

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.

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.

Spiking neural networks (SNNs) have gained significant attention for their biological plausibility, event-driven operation, and low power consumption, establishing them as a leading model for processing event stream data. However, current models often oversimplify neuronal dynamics to balance computational cost and performance. To address this limitation and enhance the dynamical behavior of spiking neurons, this paper introduces two key innovations. First, inspired by biological autaptic connections and memristive devices, we propose the memristive autapse (M-Autapse), a self-connection mechanism that enables adaptive modulation of a neuron's membrane potential. Second, recognizing the need for attention mechanisms that match SNNs' spatio-temporal nature, we design a spatio-temporal synergistic attention (STSA) mechanism to bolster simultaneous focus on both temporal and spatial dimensions of input data. Extensive experiments on the neuromorphic speech benchmarks SHD and SSC validate our methods. On SHD, our model demonstrates performance competitive with the state-of-the-art, while also achieving strong results on the SSC dataset.

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.

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.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarchical deep learning architecture, VaeTF, incorporating community-aware mechanisms based on resting-state functional magnetic resonance imaging (rs-fMRI) data. VaeTF introduces a priori knowledge of the functional community, extracts localized features through a variational auto-encoder (VAE), captures global dependencies across brain regions using the Transformer module, and incorporates an improved pooling mechanism to enhance the expressive power and model generalization performance. Experimental results on the ABIDE database show that VaeTF achieves 71.4% accuracy in ASD and typically performs well in group classification tasks. Further feature weighting analysis reveals that VaeTF is capable of identifying local functional abnormalities and cross-network functional synergistic dysfunctions closely related to ASD, thereby uncovering the underlying neurobiological mechanisms. VaeTF not only improves the classification performance of ASD but also provides a new method and theoretical support for objective assessment and early diagnosis based on fMRI.

In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.

Building upon our prior introduction of the Delay concept within a neuron-astrocyte electromagnetic coupling system, this study provides a deeper investigation into this phenomenon. The focus is on a specific time interval, termed Delay, which occurs after the cessation of external stimuli. During this period, neurons continue their firing activity before transitioning to a resting state. We initially elucidate that the prolonged neuronal firing, termed Delay, originates from astrocytic involvement rather than magnetic effects. Moreover, the periodic calcium activity of astrocytes can periodically induce the occurrence of neuronal Delay. Finally, we provide a thorough analysis of the duration and structural composition of the neuron Delay induced by astrocytes. The significance of our findings lies in the potential functional role of the Delay phase in the modulation and processing of neural information. Our findings offer a novel perspective on the complex dynamics governing the transition from active firing to resting in neurons, thereby enhancing the understanding of neural response and adaptability.

In recent years, brain-computer interface (BCI) technology has made significant progress in the fields of neural engineering and human-computer interaction. Among these advances, decoding upper-limb movements from electroencephalography (EEG) signals has become a key research focus. However, most existing studies concentrate on discrete classification tasks (e.g., motor imagery recognition), while the prediction of three-dimensional continuous movement trajectories still faces several major challenges. These include the low signal-to-noise ratio of EEG signals, substantial inter-subject variability that limits generalizability, and the high degrees of freedom in 3D trajectories, which increase decoding complexity. To address these challenges and improve the accuracy of decoding continuous 3D hand movement trajectories from EEG signals, this study proposes a Multi-level Multi-scale Temporal Attention Transformer framework (M3T-Attention). The model is designed to extract temporal features across multiple time scales from EEG signals and integrate them via cross-scale attention mechanisms, enabling a nonlinear mapping from 0.5 to 12 Hz EEG signals to 3D kinematic parameters (position, velocity, and acceleration). The model was trained using EEG and wrist kinematic data from the WAY-EEG-GAL dataset. Experimental results show that the proposed method achieves Pearson correlation coefficients (PCCs) of 0.8816, 0.8841, and 0.8711 on the X, Y, and Z axes, respectively, demonstrating robust prediction performance across all subjects and outperforming existing state-of-the-art approaches. In summary, through comparative experiments, statistical significance analysis, and ablation studies, we have fully verified its ability to capture neural coding patterns. It significantly enhances the decoding performance from EEG signals to movement trajectories, offering new approaches for BCI applications in complex motor control scenarios. We have made the model's source code publicly available on GitHub repository URL: https://github.com/jjspp/M3T_Attention.

Parkinson's disease (PD) is a neurodegenerative disease that causes extensive impacts on cognitive and motor function, hence making correct and early diagnosis essential for efficient clinical management and enhancement of the quality of life. Analysis using EEG has now become a safe and possible method for the identification of neural abnormalities in PD. Nevertheless, current models must struggle with several constraints: high false detection rates, poor generalizability across subjects, sensitivity to EEG noise pollution, and the inability to extract deep cortical representations, which have the capability to distinguish between healthy and Parkinson patterns. To alleviate these issues, the current paper proposes a novel CortiMoS-Net (Cortical Modeling with Stacked Autoencoder and MobileNet) capable of accurately detecting Parkinson's disease from EEG signals. CortiMoS-Net architecture combines deep stacked autoencoders with low-computation MobileNet convolution blocks such that low-complexity learning of complex cortical activity patterns is supplemented with computational scalability. To achieve further enhanced model convergence and optimization of learnable parameters, the present work also proposes an enhanced hybrid optimization technique named Snow Shepherd Stride Configuration Tuning (S3C-Tune). The proposed pipeline is initiated with raw EEG signal recording, preprocessing, and peak picking for the intent of artifact removal and detection of neurologically intriguing events. Model parameters are tuned by the S3C-Tune algorithm to realize maximal training accuracy. Such a pipeline hybrid enables extensive cortical modeling as well as efficient optimization and results in correct PD vs. healthy subject classification. Experimental results confirm the effectiveness of the suggested approach with better accuracy, precision, recall, and F1-score of 0.99 and minimum error rate and minimum loss of 0.01 and 0.05, respectively. The suggested model also indicates maximum prediction correctness of 0.99 and mean efficiency measure of 0.95 as compared to a large number of state-of-the-art hybrid deep learning approaches.