International journal of neural systems最新文献

Brain-computer interface (BCI) technology supports the interactions of individuals with severe neuromuscular limitations with their environment. This work presents a classification approach for distinguishing motor imagery (MI) from speech-related cognitive imagery (CI) such as word generation and arithmetic subtraction, using magnetoencephalography (MEG) signals. Differentiating MI and CI/SI processes is relevant for expanding command diversity in hybrid BCI systems and for clarifying the distinct neural mechanisms underlying motor versus verbal-semantic processing. Although a large proportion of noninvasive BCI studies focus on MI, this distinction has received relatively limited attention, particularly in MEG-based approaches. Making this distinction is important for increasing command diversity in hybrid BCI systems and for improving the understanding of neural mechanisms associated with motor and verbal-semantic processing. Tasks from an open-access MEG dataset were analyzed across six binary pairs (H-F, H-W, H-S, F-W, F-S, W-S). MEG signals were processed using two frequency-separation strategies: a broad-band configuration (FSB-1: 8-14[Formula: see text]Hz and 14-30[Formula: see text]Hz) and a narrow-band configuration (FSB-2: six sub-bands between 8 and 32[Formula: see text]Hz). Time-frequency features were extracted using continuous wavelet transform (CWT), and spatial features via the common spatial pattern (CSP) method. Feature selection followed a two-stage procedure: (i) t-test ranking to obtain a shared feature set for all task pairs; and (ii) subject- and task-specific optimization of feature number. The initial evaluation based on the shared feature set showed that the FSB-2/CWT approach yielded better classification accuracies compared to FSB-1/CWT (H-F: 56%, H-W: 71%, H-S: 66% versus 54%, 68%, 64%). With subject- and task-adaptive optimization, additional improvements were observed. Accuracies increased to 60%, 72%, and 69% for FSB-1, and to 63%, 75%, and 71% for FSB-2, for H-F, H-W, and H-S, respectively. Overall, the findings indicate that the proposed CWT[Formula: see text]CSP framework, particularly when combined with adaptive feature optimization, offers an interpretable analysis approach that can contribute to MI-CI discrimination in MEG-based BCI systems under limited data conditions.

Clinically, epilepsy manifests as a chronic condition marked by unprovoked, recurrent seizures, plaguing over 70 million individuals with debilitating seizures and life-threatening complications. Approximately 30% of patients with epilepsy do not respond to conventional antiepileptic drugs, indicating the limited efficacy of these medications in controlling seizures universally. Therefore, seizure prediction has become a key factor in enabling timely intervention for epilepsy patients, which can provide crucial time for clinical treatment and preventive measures. This study aimed to propose a lightweight seizure prediction model integrating a residual network (ResNet) with a kernel-enhanced global temporal attention Block (GTA Block). The ResNet extracts electroencephalogram (EEG) features while maintaining gradient stability, and the GTA mechanism constructs full-sequence temporal association matrices to capture the dynamic evolution of EEG patterns. Then a kernel function is embedded into GTA Block for mapping EEG samples into a high-dimensional space in which the distinction between preictal and interictal states is enhanced. The model significantly outperforms existing methods while maintaining a lightweight architecture suitable for embedded systems. With only 1.94 million parameters and an inference time of 0.00207[Formula: see text]s, this lightweight design facilitates real-time deployment on wearable devices, enhancing feasibility for continuous clinical monitoring in resource-constrained settings.

This study aims to develop a multimodal driver emotion recognition system that accurately identifies a driver's emotional state during the driving process by integrating facial expressions, ElectroCardioGram (ECG) and ElectroEncephaloGram (EEG) signals. Specifically, this study proposes a model that employs a Conformer for analyzing facial images to extract visual cues related to the driver's emotions. Additionally, two Autoformers are utilized to process ECG and EEG signals. The embeddings from these three modalities are then fused using a cross-attention mechanism. The integrated features from the cross-attention mechanism are passed through a fully connected layer and classified to determine the driver's emotional state. The experimental results demonstrate that the fusion of visual, physiological and neurological modalities significantly improves the reliability and accuracy of emotion detection. The proposed approach not only offers insights into the emotional processes critical for driver assistance systems and vehicle safety but also lays the foundation for further advancements in emotion recognition area.

Accurate localization of the theoretical epileptogenic zone in cingulate epilepsy is particularly challenging due to the region's deep anatomical location and complex connectivity. While invasive stereoelectroencephalography (sEEG) methodology offers excellent spatiotemporal sampling of deep intracerebral structures, interpretation of these high-dimensional recordings remains largely qualitative and subject to interpretation by clinician experts. To address this limitation, we propose a quantitative, biomarker-based framework using phase-amplitude coupling (PAC) to investigate 25 seizures recorded from four patients with complex cingulate epilepsy who underwent sEEG followed by surgical treatment (either laser ablation or open resection), achieving ≥ 1 year of sustained seizure freedom. PAC values were computed from sEEG electrode contacts across multiple seizures during pre-ictal and ictal phases, employing wide-frequency and band-specific frequency coupling approaches. Among frequency pairs, theta-beta ([Formula: see text]-[Formula: see text]) coupling consistently demonstrated the most robust differentiation between surgically-treated and untreated contact sites. Our findings highlight frequency-specific PAC-based metrics as a potential tool for mapping dynamic epileptiform activity in brain networks, offering quantitative insight that may refine surgical planning and decision-making in challenging cases of cingulate epilepsy.

Automatic seizure detection holds significant importance for epilepsy diagnosis and treatment. Convolutional neural networks (CNNs) have shown immense potential in seizure detection. Though traditional CNN-based seizure detection models have achieved significant advancements, they often suffer from excessive parameters and limited interpretability, thus hindering their reliability and practical deployment on edge computing devices. Therefore, this study introduces an innovative Morlet convolutional neural network (Morlet-CNN) framework with its effectiveness demonstrated in seizure detection tasks. Unlike traditional CNNs, the convolutional kernels in the Morlet-CNN contain only two learnable parameters, allowing for a lightweight architecture. Additionally, we propose a frequency-domain-response-based kernel pruning algorithm for Morlet-CNN and implement an INT8 quantization algorithm by incorporating Kullback-Leibler (KL) divergence calibration with a Morlet lookup table (LUT). With the pruning and quantization algorithms, the model's parameter scale achieves over 90% reduction while maintaining minimal accuracy loss. Furthermore, the model exhibits enhanced interpretability from a signal processing perspective, distinguishing it from many previous CNN models. Extensive experimental validation on the Bonn and CHB-MIT datasets confirms the Morlet-CNN model's efficacy with a compact Kilobyte (KB)-level model size, making it highly suitable for real-world applications.

Knowledge graphs (KGs) which represent entities and their relations in a structured manner, have become a fundamental resource for various natural language processing tasks. However, the incompleteness of KGs significantly hinders their effectiveness, thereby reducing their practical utility. The challenge of predicting missing relations between entities and performing these predictions efficiently has become a focal point of research. To address the challenge of incomplete KGs, we propose GEGS, a novel KG embedding framework that enhances scalability and expressiveness for relation prediction. GEGS introduces GAT-SNP, a graph attention network that, for the first time, integrates nonlinear spiking neural P (SNP) mechanisms into graph attention models and applies them to the KG domain, effectively capturing complex relational structures. The GAT-SNP network assigns distinct attention weights to each node, enabling the model to focus on the most relevant nodes in the graph. To mitigate information loss in long-range and sequential path features, we incorporate a BiLSTM-SNP component, which alleviates long-term dependency issues while preserving global path information. By leveraging GAT-SNP and BiLSTM-SNP, GEGS achieves superior performance in link prediction tasks, paving the way for applications in large-scale knowledge base completion. Kinship, FB15k-237, and WN18RR are used to evaluate the proposed GEGS model. The experimental results indicate that the proposed GEGS model has achieved state-of-the-art results in multiple evaluation metrics(e.g. Hits@10 and MRR).

Spiking Neural P (SNP) systems have attracted increasing attention due to their biologically inspired, event-driven computation and inherent capability for temporal modeling. However, most existing SNP variants rely on fixed or purely local information propagation mechanisms, which limits their ability to capture long-range dependencies and contextual interactions in complex structured data. To address this limitation, we propose an Attention-Gated Spiking Neural membrane system (AGSNP), which incorporates an attention-guided gating mechanism directly into the spiking neuron dynamics. Unlike prior SNP models that treat attention as an external aggregation operation, AGSNP embeds attention signals into the nonlinear spiking update and memory regulation process. This design enables adaptive information propagation across distant structural components while preserving biologically inspired spiking behavior. To evaluate the effectiveness of the proposed architecture, AGSNP is instantiated within a graph-based learning framework and applied to Structure Activity Relationship (SAR) prediction. Experiments on three publicly available benchmark datasets demonstrate that AGSNP consistently outperforms representative baseline methods. Notably, under limited data availability and severe class imbalance, the proposed model achieves improvements of approximately 2.0-5.7% in AUC and related metrics on the Tox21 dataset, and 3.5-17.0% on the MUV dataset.

Major depressive disorder (MDD) is a serious, complex psychiatric condition that affects millions of people worldwide. Early diagnosis and biomarker identification are critical for personalized treatment and effective disease monitoring. While resting-state functional magnetic resonance imaging (rs-fMRI) combined with deep learning has facilitated MDD prediction, existing methods often overlook the dynamic temporal characteristics of blood oxygen level-dependent (BOLD) signals and ignore the strength of inter-regional connections, resulting in brain region updates devoid of biological specificity. To this end, a functional-dynamic synaptic graph neural network (FDSyn-GNN) is proposed, which integrates a bidirectional gated recurrent unit (Bi-GRU) timestamp encoding (BGTE) module for modeling dynamic BOLD signals and a synaptic graph Transformer (SGT) module for connection-aware brain region updates. FDSyn-GNN is validated on two large-scale MDD datasets collected across multiple sites, where it outperforms 12 state-of-the-art (SOTA) baseline methods. In addition, extensive ablation and interpretability analyses highlight its potential for biomarker discovery, offering insights into the neural mechanisms underlying MDD. The code is publicly available at https://github.com/ZHChen-294/FDSyn-GNN.

Spiking neural P systems (SN PS) exemplify the third-generation of spiking neural networks (SNNs), which perform distributed and concurrent computations. However, SN PS rely solely on the spike as the singular signaling entity, ignoring the influence of other substances in the biological nervous system. Neuromodulators have the ability to exert their effects on the postsynaptic membrane, influencing synaptic plasticity and modifying the intensity of interneuronal connections. Motivated by this biological observation, we introduce a self-adaptation spiking neural P system with neuromodulators (SSNN PS). Specifically, neuromodulators generated by neurons are designed as resources consumed by rules in the postsynaptic membrane. The postsynaptic membrane, functioning as a new computational unit with three novel rules, possesses self-adapting weights regulated by neuromodulators and reflects the intensity of connections between neurons. Therefore, the SSNN PS enhance the control of the system over the computing process. In this work, we demonstrate the Turing universality of SSNN PS as both a number-generating and a number-accepting device. In addition, to verify the application capability, an SSNN PS for gender recognition of face images was constructed. The postsynaptic membrane self-adaptively updates its weights as it receives the feedback neuromodulators, which makes the recognition result more accurate. It achieves an accuracy of 91.71% on the UTKFace dataset and 87.83% on the FairFace dataset, and outperforms the other five comparative methods.

High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain-computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.