International Journal of Neural Systems最新文献_第3页

Epileptic Seizure Detection with an End-to-end Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model 利用端到端时态卷积网络和双向长短期记忆模型检测癫痫发作

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-12-15 DOI: 10.1142/s0129065724500126

Xingchen Dong, Yiming Wen, Dezan Ji, Shasha Yuan, Zhen Liu, Wei Shang, Weidong Zhou

引用次数: 0

A graph-based neural approach to linear sum assignment problems 用基于图的神经方法解决线性和分配问题

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-12-08 DOI: 10.1142/s0129065724500114

Carlo Aironi, Samuele Cornell, Stefano Squartini

引用次数: 0

Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks 自动评估视觉语言任务大型基准数据集的质量

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-24 DOI: 10.1142/s0129065724500096

Ruibin Zhao, Zhiwei Xie, Yipeng Zhuang, P. L. Yu

引用次数: 0

sEMG-based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin 基于 sEMG 的会话间手势识别，通过具有位置保持和最大边际的域自适应实现

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-24 DOI: 10.1142/s0129065724500102

Yao Guo, Jiayan Liu, Yonglin Wu, Xinyu Jiang, Yalin Wang, Long Meng, Xiangyu Liu, Feng Shu, Chenyun Dai, Wei Chen

引用次数: 0

Cultural Differences in the Assessment of Synthetic Voices 合成声音评估中的文化差异

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-23 DOI: 10.1142/s0129065724500084

M. Cuciniello, T. Amorese, C. Greco, Zoraida Callejas Carrión, Carl Vogel, G. Cordasco, Anna Esposito

引用次数: 0

Alzheimer's Disease Evaluation through Visual Explainability by means of Convolutional Neural Networks 通过卷积神经网络的视觉可解释性评估阿尔茨海默病

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-15 DOI: 10.1142/s0129065724500072

F. Mercaldo, Marcello Di Giammarco, Fabrizio Ravelli, Fabio Martinelli, A. Santone, M. Cesarelli

引用次数: 0

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals. 基于自适应通道优化的多视图图对比学习在脑电信号抑郁检测中的应用

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-01 DOI: 10.1142/S0129065723500557

Shuangyong Zhang, Hong Wang, Zixi Zheng, Tianyu Liu, Weixin Li, Zishan Zhang, Yanshen Sun

{"title":"Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals.","authors":"Shuangyong Zhang, Hong Wang, Zixi Zheng, Tianyu Liu, Weixin Li, Zishan Zhang, Yanshen Sun","doi":"10.1142/S0129065723500557","DOIUrl":"10.1142/S0129065723500557","url":null,"abstract":"Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity. The spatial topological structure of EEG signals has not been fully utilized to capture more fine-grained features. (3) Prior depression detection models fail to provide interpretability. To address these challenges, this paper proposes a new model, Multi-view Graph Contrastive Learning via Adaptive Channel Optimization (MGCL-ACO) for depression detection in EEG signals. Specifically, the proposed model first selects the critical channels by maximizing the mutual information between tracks and labels of EEG signals to eliminate data redundancy. Then, the MGCL-ACO model builds two similarity metric views based on functional connectivity and spatial proximity. MGCL-ACO constructs the feature extraction module by graph convolutions and contrastive learning to capture more fine-grained features of different perspectives. Finally, our model provides interpretability by visualizing a brain map related to the significance scores of the selected channels. Extensive experiments have been performed on public datasets, and the results show that our proposed model outperforms the most advanced baselines. Our proposed model not only provides a promising approach for automated depression detection using optimal EEG signals but also has the potential to improve the accuracy and interpretability of depression diagnosis in clinical practice.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46490946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Network for Patient-Specific Seizure Prediction from EEG Data. 从脑电图数据预测患者特定癫痫发作的混合网络

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-01 DOI: 10.1142/S0129065723500569

Yongfeng Zhang, Tiantian Xiao, Ziwei Wang, Hongbin Lv, Shuai Wang, Hailing Feng, Shanshan Zhao, Yanna Zhao

{"title":"Hybrid Network for Patient-Specific Seizure Prediction from EEG Data.","authors":"Yongfeng Zhang, Tiantian Xiao, Ziwei Wang, Hongbin Lv, Shuai Wang, Hailing Feng, Shanshan Zhao, Yanna Zhao","doi":"10.1142/S0129065723500569","DOIUrl":"10.1142/S0129065723500569","url":null,"abstract":"Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN based on Swin transformer (ST) and 2D convolutional neural network (2DCNN) is proposed. Time-frequency features extracted by short-term Fourier transform (STFT) are taken as the input of STCNN. ST blocks are used in STCNN to capture the global information and long-term dependencies of EEGs. Meanwhile, the 2DCNN blocks are adopted to capture the local information and short-term dependent features. The combination of the two blocks can fully exploit the seizure-related information thus improve the prediction performance. Comprehensive experiments are performed on the CHB-MIT scalp EEG dataset. The average seizure prediction sensitivity, the area under the ROC curve (AUC) and the false positive rate (FPR) are 92.94%, 95.56% and 0.073, respectively.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46172061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Epileptic Seizure Prediction Using Attention Augmented Convolutional Network. 使用注意力增强卷积网络预测癫痫发作。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-11-01 Epub Date: 2023-09-07 DOI: 10.1142/S0129065723500545

Dongsheng Liu, Xingchen Dong, Dong Bian, Weidong Zhou

{"title":"Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.","authors":"Dongsheng Liu, Xingchen Dong, Dong Bian, Weidong Zhou","doi":"10.1142/S0129065723500545","DOIUrl":"10.1142/S0129065723500545","url":null,"abstract":"Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10161304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer. 通过开发自适应迭代线性约束最小方差波束形成器定位癫痫脑对单脉冲电刺激的反应。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2023-10-01 Epub Date: 2023-08-09 DOI: 10.1142/S0129065723500508

Sepehr Shirani, Antonio Valentin, Bahman Abdi-Sargezeh, Gonzalo Alarcon, Saeid Sanei

{"title":"Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer.","authors":"Sepehr Shirani, Antonio Valentin, Bahman Abdi-Sargezeh, Gonzalo Alarcon, Saeid Sanei","doi":"10.1142/S0129065723500508","DOIUrl":"10.1142/S0129065723500508","url":null,"abstract":"Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":null,"pages":null},"PeriodicalIF":8.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9979245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2