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

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

Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation. 用神经记忆常微分方程增强医学图像分割模型的鲁棒性。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-09-23 DOI: 10.1142/S0129065723500600

Junjie Hu, Chengrong Yu, Zhang Yi, Haixian Zhang

{"title":"Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation.","authors":"Junjie Hu, Chengrong Yu, Zhang Yi, Haixian Zhang","doi":"10.1142/S0129065723500600","DOIUrl":"10.1142/S0129065723500600","url":null,"abstract":"Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350060"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180733","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

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach. 使用集中时频方法对癫痫脑电图信号进行基于深度学习的分类。

International journal of neural systems Pub Date : 2023-12-01 Epub Date: 2023-10-13 DOI: 10.1142/S0129065723500648

Mosab A A Yousif, Mahmut Ozturk

{"title":"Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.","authors":"Mosab A A Yousif, Mahmut Ozturk","doi":"10.1142/S0129065723500648","DOIUrl":"10.1142/S0129065723500648","url":null,"abstract":"ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT. The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2350064"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224085","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

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection 基于深度学习的双峰特征分析用于自闭症谱系障碍检测

International journal of neural systems Pub Date : 2023-11-07 DOI: 10.1142/s0129065724500059

Federica Colonnese, Francesco Di Luzio, Antonello Rosato, Massimo Panella

引用次数: 0

Discriminative Power of Handwriting and Drawing Features in Depression 抑郁症患者笔迹与绘画特征的辨别力

International journal of neural systems Pub Date : 2023-11-07 DOI: 10.1142/s0129065723500697

Claudia Greco, Gennaro Raimo, Terry Amorese, Marialucia Cuciniello, Gavin Mcconvey, Gennaro Cordasco, Marcos Faundez-Zanuy, Alessandro Vinciarelli, Zoraida Callejas-Carrion, Anna Esposito

{"title":"Discriminative Power of Handwriting and Drawing Features in Depression","authors":"Claudia Greco, Gennaro Raimo, Terry Amorese, Marialucia Cuciniello, Gavin Mcconvey, Gennaro Cordasco, Marcos Faundez-Zanuy, Alessandro Vinciarelli, Zoraida Callejas-Carrion, Anna Esposito","doi":"10.1142/s0129065723500697","DOIUrl":"https://doi.org/10.1142/s0129065723500697","url":null,"abstract":"This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"71 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545032","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