IEEE Transactions on Cognitive and Developmental Systems最新文献_第2页

IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459314

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IEEE Transactions on Cognitive and Developmental Systems Information for Authors 电气和电子工程师学会《认知与发展系统》期刊为作者提供的信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459316

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IEEE Transactions on Cognitive and Developmental Systems Publication Information 电气和电子工程师学会认知与发展系统论文集》出版信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-10-14 DOI: 10.1109/TCDS.2024.3459312

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Embodied Perception Interaction, and Cognition for Wearable Robotics: A Survey 可穿戴机器人的嵌入式感知、交互和认知：调查

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-18 DOI: 10.1109/tcds.2024.3463194

Xiaoyu Wu, Jiale Liang, Yiang Yu, Guoxin Li, Gary G. Yen, Haoyong Yu

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CS-SLAM: A lightweight semantic SLAM method for dynamic scenarios CS-SLAM：适用于动态场景的轻量级语义 SLAM 方法

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-17 DOI: 10.1109/tcds.2024.3462651

Zhendong Guo, Na Dong, Zehui Zhang, Xiaoming Mai, Donghui Li

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Edge-centric-functional-connectivity based cofluctuation-guided subcortical functional connectivity network construction 以边缘为中心、基于共波动引导的皮层下功能连接网络构建

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-17 DOI: 10.1109/tcds.2024.3462709

Qinrui Ling, Aiping Liu, Taomian Mi, Piu Chan, Xun Chen

引用次数: 0

Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding Into Text 揭开思想的面纱：脑电图脑信号解码为文本的进展回顾

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-17 DOI: 10.1109/TCDS.2024.3462452

Saydul Akbar Murad;Nick Rahimi

{"title":"Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding Into Text","authors":"Saydul Akbar Murad;Nick Rahimi","doi":"10.1109/TCDS.2024.3462452","DOIUrl":"10.1109/TCDS.2024.3462452","url":null,"abstract":"The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It is important to outline this area's recent developments and future research directions to provide a comprehensive understanding of the current state of technology, guide future research efforts, and enhance the effectiveness and accessibility of EEG-to-text systems. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. First, we talk about how EEG-to-text technology has grown and what problems the field still faces. Second, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective brain–computer interface (BCI) technology for a broader user base.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"61-76"},"PeriodicalIF":5.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Mental Workload Assessment Using Deep Learning Models From EEG Signals: A Systematic Review

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-16 DOI: 10.1109/TCDS.2024.3460750

Kunjira Kingphai;Yashar Moshfeghi

{"title":"Mental Workload Assessment Using Deep Learning Models From EEG Signals: A Systematic Review","authors":"Kunjira Kingphai;Yashar Moshfeghi","doi":"10.1109/TCDS.2024.3460750","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3460750","url":null,"abstract":"Mental workload (MWL) assessment is crucial in information systems (IS), impacting task performance, user experience, and system effectiveness. Deep learning offers promising techniques for MWL classification using electroencephalography (EEG), which monitors cognitive states dynamically and unobtrusively. Our research explores deep learning's potential and challenges in EEG-based MWL classification, focusing on training inputs, cross-validation methods, and classification problem types. We identify five types of EEG-based MWL classification: within-subject, cross subject, cross session, cross task, and combined cross task and cross subject. Success depends on managing dataset uniqueness, session and task variability, and artifact removal. Despite the potential, real-world applications are limited. Enhancements are necessary for self-reporting methods, universal preprocessing standards, and MWL assessment accuracy. Specifically, inaccuracies are inflated when data are shuffled before splitting to train and test sets, disrupting EEG signals’ temporal sequence. In contrast, methods such as the time-series cross validation and leave-session-out approach better preserve temporal integrity, offering more accurate model performance evaluations. Utilizing deep learning for EEG-based MWL assessment could significantly improve IS functionality and adaptability in real time based on user cognitive states.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"40-60"},"PeriodicalIF":5.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fatigue state recognition system for miners based on a multi-modal feature extraction and fusion framework 基于多模态特征提取和融合框架的矿工疲劳状态识别系统

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-16 DOI: 10.1109/tcds.2024.3461713

Hongguang Pan, Shiyu Tong, Xuqiang Wei, Bingyang Teng

引用次数: 0

Neighborhood curiosity-based exploration in multi-agent reinforcement learning 多代理强化学习中基于好奇心的邻域探索

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-09-13 DOI: 10.1109/tcds.2024.3460368

Shike Yang, Ziming He, Jingchen Li, Haobin Shi, Qingbing Ji, Kao-Shing Hwang, Xianshan Li

引用次数: 0