Neurocomputing最新文献

EEG-based epileptic seizure detection using deep learning techniques: A survey 使用深度学习技术进行基于脑电图的癫痫发作检测：一项调查

IF 6 2区计算机科学

Neurocomputing Pub Date : 2024-09-17 DOI: 10.1016/j.neucom.2024.128644

Jie Xu, Kuiting Yan, Zengqian Deng, Yankai Yang, Jin-Xing Liu, Juan Wang, Shasha Yuan

{"title":"EEG-based epileptic seizure detection using deep learning techniques: A survey","authors":"Jie Xu, Kuiting Yan, Zengqian Deng, Yankai Yang, Jin-Xing Liu, Juan Wang, Shasha Yuan","doi":"10.1016/j.neucom.2024.128644","DOIUrl":"https://doi.org/10.1016/j.neucom.2024.128644","url":null,"abstract":"Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and spatial shifts in cerebral electrical activity, holding pivotal diagnostic and therapeutic value for epilepsy. Deep learning techniques have made remarkable progress in EEG-based seizure detection over recent years. This review is dedicated to exploring seizure detection approaches based on deep learning, focusing on three distinct avenues. Primarily, we delve into the application of canonical deep learning methods in epilepsy detection. Subsequently, a more in-depth study was conducted on the hybrid models of deep learning. Next, the third is the integration of deep learning and traditional machine learning strategies. Finally, the challenges and future prospects related to this topic are put forward. The uniqueness of this review lies in its novel and comprehensive perspective on the latest research on deep learning-based epilepsy detection by systematically classifying methods, visualizing research progress, and addressing challenges and gaps in current research. It can provide valuable guidance for researchers who want to delve into the field of epileptic seizure detection based on EEG signals.","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254313","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

Towards sharper excess risk bounds for differentially private pairwise learning 为差异化私人成对学习设定更敏锐的超额风险边界

IF 6 2区计算机科学

Neurocomputing Pub Date : 2024-09-17 DOI: 10.1016/j.neucom.2024.128610

Yilin Kang, Jian Li, Yong Liu, Weiping Wang

引用次数: 0

Finite-time synchronization of proportional delay memristive competitive neural networks 比例延迟记忆竞争神经网络的有限时间同步

IF 6 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128612

Jiapeng Han, Liqun Zhou

引用次数: 0

Group-feature (Sensor) selection with controlled redundancy using neural networks 利用神经网络选择受控冗余的组特征（传感器

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128596

引用次数: 0

SDD-Net: Soldering defect detection network for printed circuit boards SDD-Net：印刷电路板焊接缺陷检测网络

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128575

引用次数: 0

Adaptive denoising graph contrastive learning with memory graph attention for recommendation 利用记忆图关注推荐的自适应去噪图对比学习

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128595

{"title":"Adaptive denoising graph contrastive learning with memory graph attention for recommendation","authors":"","doi":"10.1016/j.neucom.2024.128595","DOIUrl":"10.1016/j.neucom.2024.128595","url":null,"abstract":"<div><p>Graph contrastive learning has emerged as a powerful technique for dealing with graph noise and mining latent information in networks, that has been widely applied in GNN-based collaborative filtering. Traditional graph contrastive learning methods commonly generate multiple augmented views, and then learn node representations by maximizing the consistency between these views. However, on one hand, manual view construction methods necessitate expert knowledge and a trial-and-error process. On the other hand, adaptive view construction methods require decoders which results in increased training costs. To address the aforementioned limitations, in this paper, we propose the Adaptive Denoising Graph Contrastive Learning with Memory Graph Attention for Recommendation (ADGA) framework. Firstly, we introduce the memory graph attention mechanism to capture node attention during multi-hop information aggregation. Then, unlike previous methods that required additional node representations to generate views, ADGA proposes, for the first time, directly using attention to adaptively generate structure-aware contrastive learning views. It reduces the training cost of the model and improves the cross-view consistency of node representations, that offers a new paradigm for adaptive graph contrastive learning. Experimental results on three real-world datasets demonstrate that ADGA achieves state-of-the-art performance in recommendation tasks. The code is available at <span><span>https://github.com/Andrewsama/ADGA</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244047","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

K-order echo-type spiking neural P systems for time series forecasting 用于时间序列预测的 K 阶回声型尖峰神经 P 系统

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128613

{"title":"K-order echo-type spiking neural P systems for time series forecasting","authors":"","doi":"10.1016/j.neucom.2024.128613","DOIUrl":"10.1016/j.neucom.2024.128613","url":null,"abstract":"<div><p>Nonlinear spiking neural P (NSNP) systems are variants of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems can show rich nonlinear dynamics. This study proposes a new variant of NSNP systems, called <span><math><mi>k</mi></math></span>-order NSNP systems, and derives their mathematical models. The <span><math><mi>k</mi></math></span>-order NSNP systems are able to remember the states of the previous <span><math><mi>k</mi></math></span> moments. Based on the <span><math><mi>k</mi></math></span>-order NSNP systems, we propose a new recurrent-like model, called <span><math><mi>k</mi></math></span>-order echo-type spiking neural P systems or termed kESNP model. Structurally, the <span><math><mi>k</mi></math></span>ESNP model is a <span><math><mi>k</mi></math></span>-order NSNP system equipped with an input layer and an output layer. Inspired by echo state networks (ESN), this <span><math><mi>k</mi></math></span>ESNP model is trained by ridge regression algorithm. Six time series are used as benchmark data sets to evaluate the <span><math><mi>k</mi></math></span>ESNP model and it is compared with 33 baseline prediction methods. The experimental results demonstrate that the proposed <span><math><mi>k</mi></math></span>ESNP model is sufficient for the task of time series forecasting.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244050","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

A parallel neural networks for emotion recognition based on EEG signals 基于脑电信号的并行情绪识别神经网络

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128624

{"title":"A parallel neural networks for emotion recognition based on EEG signals","authors":"","doi":"10.1016/j.neucom.2024.128624","DOIUrl":"10.1016/j.neucom.2024.128624","url":null,"abstract":"<div><p>Our study proposes a novel Parallel Temporal–Spatial-Frequency Neural Network (PTSFNN) for emotion recognition. The network processes EEG signals in the time, frequency, and spatial domains simultaneously to extract discriminative features. Despite its relatively simple architecture, the proposed model achieves superior performance. Specifically, PTSFNN first applies wavelet transform to the raw EEG signals and then reconstructs the coefficients based on frequency hierarchy, thereby achieving frequency decomposition. Subsequently, the core part of the network performs three independent parallel convolution operations on the decomposed signals, including a novel graph convolutional network. Finally, an attention mechanism-based post-processing operation is designed to effectively enhance feature representation. The features obtained from the three modules are concatenated for classification, with the cross-entropy loss function being adopted. To evaluate the model’s performance, extensive experiments are conducted on the SEED and SEED-IV public datasets. The experimental results demonstrate that PTSFNN achieves excellent performance in emotion recognition tasks, with classification accuracies of 87.63% and 74.96%, respectively. Comparative experiments with previous state-of-the-art methods confirm the superiority of our proposed model, which can efficiently extract emotion information from EEG signals.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244207","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

JARViS: Detecting actions in video using unified actor-scene context relation modeling JARViS：利用统一的演员-场景上下文关系建模检测视频中的动作

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128616

{"title":"JARViS: Detecting actions in video using unified actor-scene context relation modeling","authors":"","doi":"10.1016/j.neucom.2024.128616","DOIUrl":"10.1016/j.neucom.2024.128616","url":null,"abstract":"<div><p>Video action detection (VAD) is a formidable vision task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. Among the myriad VAD architectures, two-stage VAD methods utilize a pre-trained person detector to extract the region of interest features, subsequently employing these features for action detection. However, the performance of two-stage VAD methods has been limited as they depend solely on localized actor features to infer action semantics. In this study, we propose a new two-stage VAD framework called Joint Actor-scene context Relation modeling based on Visual Semantics (JARViS), which effectively consolidates cross-modal action semantics distributed globally across spatial and temporal dimensions using Transformer attention. JARViS employs a person detector to produce densely sampled actor features from a keyframe. Concurrently, it uses a video backbone to create spatio-temporal scene features from a video clip. Finally, the fine-grained interactions between actors and scenes are modeled through a Unified Action-Scene Context Transformer to directly output the final set of actions in parallel. Our experimental results demonstrate that JARViS outperforms existing methods by significant margins and achieves state-of-the-art performance on three popular VAD datasets, including AVA, UCF101-24, and JHMDB51-21.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244053","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

Finite-time passivity of multi-weighted coupled neural networks with directed topologies and time-varying delay 具有定向拓扑和时变延迟的多权重耦合神经网络的有限时间被动性

IF 5.5 2区计算机科学

Neurocomputing Pub Date : 2024-09-16 DOI: 10.1016/j.neucom.2024.128581

引用次数: 0