International journal of neural systems最新文献_第4页

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics 增强多任务学习以生成掌纹生物识别哈希代码

International journal of neural systems Pub Date : 2024-01-19 DOI: 10.1142/s0129065724500205

Lin Chen, Lu Leng, Ziyuan Yang, Andrew Beng Jin Teoh

引用次数: 0

Striatum- and cerebellum-modulated epileptic networks varying across states with and without interictal epileptic discharges 有发作间期癫痫放电和无发作间期癫痫放电时，纹状体和小脑调制的癫痫网络各不相同

International journal of neural systems Pub Date : 2024-01-19 DOI: 10.1142/s0129065724500175

Sisi Jiang, Haonan Pei, Junxia Chen, Hechun Li, Zetao Liu, Yuehan Wang, Jinnan Gong, Sheng Wang, Qifu Li, M. Duan, V. Calhoun, Dezhong Yao, Cheng Luo

引用次数: 0

Multimodal covariance network reflects individual cognitive flexibility 多模态协方差网络反映个体认知的灵活性

International journal of neural systems Pub Date : 2024-01-19 DOI: 10.1142/s0129065724500187

Lin Jiang, S. Eickhoff, S. Genon, Guangying Wang, Chanlin Yi, Runyang He, Xunan Huang, Dezhong Yao, Debo Dong, Fali Li, Peng Xu

引用次数: 0

Multi-semantic decoding of visual perception with graph neural network 利用图神经网络对视觉感知进行多语义解码

International journal of neural systems Pub Date : 2024-01-12 DOI: 10.1142/s0129065724500163

Rong Li, Jiyi Li, Chong Wang, Haoxiang Liu, Tao Liu, Xuyang Wang, Ting Zou, Wei Huang, Hongmei Yan, Huafu Chen

引用次数: 0

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection. 基于脑电图的大规模驾驶员困倦检测的高效组联邦学习框架。

International journal of neural systems Pub Date : 2024-01-01 Epub Date: 2023-11-15 DOI: 10.1142/S0129065724500035

Xinyuan Chen, Yi Niu, Yanna Zhao, Xue Qin

{"title":"An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.","authors":"Xinyuan Chen, Yi Niu, Yanna Zhao, Xue Qin","doi":"10.1142/S0129065724500035","DOIUrl":"10.1142/S0129065724500035","url":null,"abstract":"To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different levels of groups and gradually aggregating their model parameters from low-level groups to high-level groups, communication and time costs are reduced. In addition, to solve the problem of notable variations in EEG signals among different clients, a global-personalized deep neural network is designed. The global model extracts shared features from various clients, while the personalized model extracts fine-grained features from each client and outputs classification results. Finally, to address special issues such as scale/category imbalance and data pollution, three checking modules are designed for adjusting grouping, evaluating client data, and effectively applying personalized models. Through extensive experimentation, the effectiveness of each component within the framework was validated, and a mean accuracy, F1-score, and Area Under Curve (AUC) of 81.0%, 82.0%, and 87.9% was achieved, respectively, on a publicly available dataset comprising 11 subjects.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450003"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107593148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals. 从皮层信号对运动进行稳定解码的无监督神经簇对齐。

International journal of neural systems Pub Date : 2024-01-01 Epub Date: 2023-12-06 DOI: 10.1142/S0129065724500060

Mohammadali Ganjali, Alireza Mehridehnavi, Sajed Rakhshani, Abed Khorasani

{"title":"Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals.","authors":"Mohammadali Ganjali, Alireza Mehridehnavi, Sajed Rakhshani, Abed Khorasani","doi":"10.1142/S0129065724500060","DOIUrl":"10.1142/S0129065724500060","url":null,"abstract":"The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2450006"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138815299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Seizure Detection Based on Multi-Scale Channel Attention. 基于多尺度通道注意力的轻型癫痫检测。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-10-17 DOI: 10.1142/S0129065723500612

Ziwei Wang, Sujuan Hou, Tiantian Xiao, Yongfeng Zhang, Hongbin Lv, Jiacheng Li, Shanshan Zhao, Yanna Zhao

{"title":"Lightweight Seizure Detection Based on Multi-Scale Channel Attention.","authors":"Ziwei Wang, Sujuan Hou, Tiantian Xiao, Yongfeng Zhang, Hongbin Lv, Jiacheng Li, Shanshan Zhao, Yanna Zhao","doi":"10.1142/S0129065723500612","DOIUrl":"10.1142/S0129065723500612","url":null,"abstract":"Epilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware, which makes them unsuitable for deployment on devices with limited resources due to their high demands on computer resources. In this paper, we propose a novel lightweight neural network for seizure detection using pure convolutions, which is composed of inverted residual structure and multi-scale channel attention mechanism. Compared with other methods, our approach significantly reduces the computational complexity, making it possible to deploy on low-cost portable devices for seizures detection. We conduct experiments on the CHB-MIT dataset and achieves 98.7% accuracy, 98.3% sensitivity and 99.1% specificity with 2.68[Formula: see text]M multiply-accumulate operations (MACs) and only 88[Formula: see text]K parameters.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350061"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41242618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG. 使用来自1或2通道EEG的频率特征的眼睛状态检测。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-10-12 DOI: 10.1142/S0129065723500624

Francisco Laport, Adriana Dapena, Paula M Castro, Daniel I Iglesias, Francisco J Vazquez-Araujo

{"title":"Eye State Detection Using Frequency Features from 1 or 2-Channel EEG.","authors":"Francisco Laport, Adriana Dapena, Paula M Castro, Daniel I Iglesias, Francisco J Vazquez-Araujo","doi":"10.1142/S0129065723500624","DOIUrl":"10.1142/S0129065723500624","url":null,"abstract":"Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350062"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers. 一个由Transformers支持的混合在线-离线策略强化学习代理框架。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-10-20 DOI: 10.1142/S012906572350065X

Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, Luis Jimenez-Linares, David Muñoz-Valero, Jun Liu

{"title":"A Hybrid Online Off-Policy Reinforcement Learning Agent Framework Supported by Transformers.","authors":"Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, Luis Jimenez-Linares, David Muñoz-Valero, Jun Liu","doi":"10.1142/S012906572350065X","DOIUrl":"10.1142/S012906572350065X","url":null,"abstract":"Reinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a framework that uses Transformers to enhance the training of online off-policy RL agents and address the challenges described above through self-attention. The proposal introduces a hybrid agent with a mixed policy that combines an online off-policy agent with an offline Transformer agent using the Decision Transformer architecture. By sequentially exchanging the experience replay buffer between the agents, the agent's learning training efficiency is improved in the first iterations and so is the training of Transformer-based RL agents in situations with limited data availability or unknown environments.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350065"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Announcement: The 2023 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. 公告：2023年霍贾特·阿德利神经系统杰出贡献奖。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-10-13 DOI: 10.1142/S0129065723820014

引用次数: 0