International journal of neural systems最新文献

Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing the complex dynamics of Electroencephalogram (EEG) signals and generalizing across diverse patient populations. In this study, the graph attention network and the transformer model are integrated for EEG signal classification, leveraging the enhanced capability to dynamically compute attention weights and adapt to the variable relevance of brain regions. The proposed approach is capable of modeling the intricate relationships within EEG activities by learning context-dependent attention scores. We conducted a comprehensive evaluation of the proposed approach comparing with the state-of-the-art algorithms. Experimental outcomes show that it surpasses the competing models. The superior performance is attributed to the proposed approach's dynamic attention mechanism, which better captures the nuanced patterns in EEG signals across different subjects and seizure types. In the experiments, the CHB-MIT dataset was exploited, which served as a benchmark for evaluating the performance of the proposed framework in distinguishing interictal, ictal, and normal EEG patterns. The results prove the usefulness of our work in advancing EEG signal classification. The findings suggest that the combination of graph attention and self-attention mechanisms is a promising approach for improving the accuracy and reliability of EEG-based diagnostics, potentially improving the management of neurological disorders.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making precise diagnoses and providing timely treatment for epilepsy. However, conventional automatic seizure detection methods often face limitations in simultaneously capturing both local features and long-range correlations inherent in EEG signals, which constrains the accuracy of these existing detection systems. To address this challenge, we propose a novel end-to-end seizure detection framework, named CNN-ViT, which complementarily integrates a Convolutional Neural Network (CNN) for capturing local inductive bias of EEG and Vision Transformer (ViT) for further mining their long-range dependency. Initially, raw electroencephalogram (EEG) signals are filtered and segmented and then sent into the CNN-ViT model to learn their local and global feature representations and identify the seizure patterns. Meanwhile, we adopt a global max-pooling strategy to reduce the scale of the CNN-ViT model and make it focus on the most discriminative features. Given the occurrence of diverse artifacts in long-term EEG recordings, we further employ post-processing techniques to improve the seizure detection performance. The proposed CNN-ViT model, when evaluated using the publicly accessible CHB-MIT EEG dataset, reveals its outstanding performance with a sensitivity of 99.34% at a segment-based level and 99.70% at an event-based level. On the SH-SDU dataset we collected, our method yielded a segment-based sensitivity of 99.86%, specificity of 94.33%, and accuracy of 94.40%, along with an event-based sensitivity of 100%. The total processing time for 1[Formula: see text]h EEG data was only 3.07[Formula: see text]s. These exceptional results demonstrate the potential of our method as a reference for clinical real-time seizure detection applications.

This paper describes a study that analyzes the effect of periodic binaural auditory stimulation in the beta band on two of the major motor symptoms of patients with Parkinson's disease (PD), resting tremor and bradykinesia. Participants included two groups of PD patients ([Formula: see text], age [Formula: see text], stage [Formula: see text] Hoehn & Yahr scale) that were exposed to an experimental (group A) or placebo (group B) auditory stimulation once a day, and a group of healthy controls ([Formula: see text], age [Formula: see text]) that was not exposed to any stimulation. The experimental stimulation consisted of 10[Formula: see text]min of binaural beats at 14[Formula: see text]Hz presented rhythmically and masked with pink noise, while the placebo stimulation consisted of pink noise only. All participants were monitored using wearable devices and mobile phones to assess the evolution of resting tremors and bradykinesia. Both indicators were obtained from accelerometer signals during the execution of specific motor tasks extracted from the MDS-UPDRS scale Part III once a week. The results show a significant difference between the group of healthy controls and PD patients for the resting tremor and bradykinesia indicators, suggesting the predictive validity of the monitoring system and the consistency of the indicators. Regarding the effect of auditory stimulation, a reduction in the level of resting tremor was observed in patients who received the experimental stimulation compared to those who received the placebo stimulation [Formula: see text] over the course of the 8 weeks of monitoring. However, no improvement in bradykinesia was observed. The generalization of results is compromised due to a set of limitations that have been identified, so guidance is provided that might contribute to improving future experimental designs in similar studies.

Social robots are increasingly being used in therapeutic contexts, especially as a complement in the therapy of children with Autism Spectrum Disorder (ASD). Because of this, the aim of this study is to understand how children with ASD perceive and interpret the gestures made by the robot Pepper versus human instructor, which can also be influenced by verbal communication. This study analyzes the impact of both conditions (verbal and nonverbal communication) and types of gestures (conversational and emotional) on gesture recognition through the study of the accuracy rate and examines the physiological responses of children with the Empatica E4 device. The results reveal that verbal communication is more accessible to children with ASD and neurotypicals (NT), with emotional gestures being more interpretable than conversational gestures. The Pepper robot was found to generate lower responses of emotional arousal compared to the human instructor in both ASD and neurotypical children. This study highlights the potential of robots like Pepper to support the communication skills of children with ASD, especially in structured and predictable nonverbal gestures. However, the findings also point to challenges, such as the need for more reliable robotic communication methods, and highlight the importance of changing interventions tailored to individual needs.

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction systems. Recent advancements in deep neural networks (DNNs) have demonstrated significant promise in this respect due to their ability to model complex nonlinear relationships. However, DNNs face persistent challenges in addressing the inter-person variability inherent in EEG signals, which limits their generalizability. To tackle this limitation, we propose a novel framework that integrates conditional identification information, leveraging the interaction between EEG signals and individual traits to enhance the model's internal representation and improve decoding accuracy. Building on this foundation, we further introduce a privacy-preserving conditional information generator - a generative model that derives embedding knowledge directly from raw EEG signals. This approach eliminates the need for personal identification via individual tests, ensuring both efficiency and privacy. Experimental evaluations conducted on WithMe dataset confirm that this framework outperforms baseline network architectures. Notably, our approach achieves substantial improvements in decoding accuracy for both familiar and unseen subjects, paving the way for efficient, robust, and privacy-conscious human-computer interface systems.

Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years, significant progress has been made in this field. However, the predictive performance of existing methods encounters a bottleneck that is difficult to overcome. Moreover, there are certain limitations such as significant differences in prediction efficacy among patients or intricate model structures. Given these considerations, Siamese Network (SiaNet) and Triplet Network (TriNet) are proposed based on tiny convolutional neural network and supervised contrastive learning. Short-Time Fourier Transform (STFT) is first applied to the pre-processed data. Then data tuples are constructed and fed into the networks for training. Both networks try to minimize the interval between samples of the same class while maximize the interval between samples of different classes. The two networks consist of multiple branches with shared weights, which can learn from each other via contrastive learning. Promising results are obtained on the CHB-MIT and Siena datasets, with a total of 35 patients. Meanwhile, both models have only 19.351K parameters.

Neuroimaging techniques have had a major impact on medical science, allowing advances in the research of many neurological diseases and improving their diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the functional activity of the brain processes related to low-level language processing of skilled and dyslexic seven-year-old readers. We have transformed EEG signals into image sequences considering the interaction between different frequency bands by means of cross-frequency coupling (CFC), and applied an activation mask sequence obtained from the local functional brain activity inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that allows differentiation between controls and dyslexic subjects with an AUC of 77.1%. Finally, explainability is improved by introducing an easily comprehensible representation of the SHAP values obtained for the classification method in the brainSHAP maps.

Deep neural networks struggle with incremental updates due to catastrophic forgetting, where newly acquired knowledge interferes with the learned previously. Continual learning (CL) methods aim to overcome this limitation by effectively updating the model without losing previous knowledge, but they find it difficult to continuously maintain knowledge about previous tasks, resulting from overlapping stored information. In this paper, we propose a CL method that preserves previous knowledge as multivariate Gaussian distributions by independently storing the model's outputs per class and continually reproducing them for future tasks. We enhance the discriminability between classes and ensure the plasticity for future tasks by exploiting contrastive learning and representation regularization. The class-wise spatial means and covariances, distinguished in the latent space, are stored in memory, where the previous knowledge is effectively preserved and reproduced for incremental tasks. Extensive experiments on benchmark datasets such as CIFAR-10, CIFAR-100, and ImageNet-100 demonstrate that the proposed method achieves accuracies of 93.21%, 77.57%, and 78.15%, respectively, outperforming state-of-the-art CL methods by 2.34 %p, 2.1 %p, and 1.91 %p. Additionally, it achieves the lowest mean forgetting rates across all datasets.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postoperative mortality. The detection and prediction of BS can help expedite the evaluation of patient conditions, optimize anesthesia administration, and improve patient safety. This study explores the potential for automatic BS detection using intraoperative EEG and BS prediction using preoperative EEG signals and patient characteristics. A dataset comprising 287 patients who underwent carotid endarterectomy procedures at Maastricht University Medical Center+ was analyzed. An EEG toolbox developed by T. Zhan at the Massachusetts Institute of Technology was utilized for the automatic detection/annotation of BS, while five machine learning classifiers were employed to predict BS occurrence using preoperative data. Based on the 160 patients manually annotated by EEG experts (regarding the presence or absence of BS), the automatic detection tool demonstrated an accuracy of 0.75. For the BS prediction task, an initial subset of 120 patients was evaluated, showing modest performance, with the K-nearest neighbors ([Formula: see text]) classifier achieving the best results, with an accuracy of 0.72. Subsequent experiments indicated that increasing the number of patients (by using Zhan's Toolbox to annotate the unlabeled instances), applying SMOTE to balance the training set, and enriching the feature set was beneficial. The final experiment demonstrated a significant improvement, with Random Forest and Gradient Boosting outperforming other classifiers, achieving an accuracy of 0.86 and ROC-AUC of 0.94. Patient characteristics, including type of anesthetic agents, symptoms, age, mean absolute delta power, mean absolute theta power, and cognitive impairment, were identified by an xAI method as important features potentially indicating the predisposition to experience BS.