IEEE Transactions on Human-Machine Systems最新文献

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-10-10 DOI: 10.1109/THMS.2025.3614336

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IEEE Transactions on Human-Machine Systems Information for Authors IEEE人机系统信息汇刊

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-10-10 DOI: 10.1109/THMS.2025.3614338

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TechRxiv: Share Your Preprint Research with the World! techxiv：与世界分享你的预印本研究！

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3583351

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IEEE Transactions on Human-Machine Systems Information for Authors IEEE人机系统信息汇刊

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3581253

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3581251

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3581249

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Present a World of Opportunity 呈现一个充满机会的世界

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3583398

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Call for Papers: IEEE Transactions on Human-Machine Systems 论文征集：IEEE人机系统汇刊

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-06-26 DOI: 10.1109/THMS.2025.3581300

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Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles 注意解释：信息类型和错误类型影响自动驾驶车辆的信任和态势感知

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-04-30 DOI: 10.1109/THMS.2025.3558437

Yaohan Ding;Lesong Jia;Na Du

{"title":"Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles","authors":"Yaohan Ding;Lesong Jia;Na Du","doi":"10.1109/THMS.2025.3558437","DOIUrl":"https://doi.org/10.1109/THMS.2025.3558437","url":null,"abstract":"Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type: <italic>why, <italic>how, <italic>why + how) × 3 (error type: false alarm, miss, correct [no error]) mixed design. <italic>How information describes the vehicle's action, while <italic>why information refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared with <italic>why and <italic>why + how information, <italic>how information was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend including <italic>why information in AV explanations and deploying AV decision systems that are less miss-prone.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"450-459"},"PeriodicalIF":3.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492389","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}

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Imagined Speech–EEG Detection Using Multivariate Swarm Sparse Decomposition-Based Joint Time–Frequency Analysis for Intuitive BCI 基于多元群稀疏分解联合时频分析的直观脑机接口想象语音-脑电检测

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-04-25 DOI: 10.1109/THMS.2025.3554449

Shailesh Vitthalrao Bhalerao;Ram Bilas Pachori

{"title":"Imagined Speech–EEG Detection Using Multivariate Swarm Sparse Decomposition-Based Joint Time–Frequency Analysis for Intuitive BCI","authors":"Shailesh Vitthalrao Bhalerao;Ram Bilas Pachori","doi":"10.1109/THMS.2025.3554449","DOIUrl":"https://doi.org/10.1109/THMS.2025.3554449","url":null,"abstract":"In brain–computer interface (BCI) applications, imagined speech (IMS) decoding based on electroencephalogram (EEG) has established a new neuro-paradigm that offers an intuitive communication tool for physically impaired patients. However, existing IMS–EEG-based BCI systems have introduced difficulties in feasible deployment due to nonstationary EEG signals, suboptimal feature extraction, and limited multiclass scalability. To address these challenges, we have presented a novel approach using the multivariate swarm-sparse decomposition method (MSSDM) for joint time–frequency (JTF) analysis and further developed a feasible end-to-end framework from multichannel IMS–EEG signals for IMS detection. MSSDM employs improved multivariate swarm filtering and sparse spectrum techniques to design optimal filter banks for extracting an ensemble of channel-aligned oscillatory components (CAOCs), significantly enhancing IMS activation-related sub-bands. To enhance channel-aligned information, multivariate JTF images have been constructed using JIF and instantaneous amplitude across channels from the obtained CAOCs. Further, JTF-based deep features (JTFDFs) were computed using different pretrained neural networks and mapped most discriminant features using two well-known feature correlation techniques: Canonical correlation analysis and Hellinger distance-based correlation. The proposed method has been tested on the 5-class BCI competition and 6-class Coretto IMS datasets. The experimental findings on cross-subject and cross-dataset reveal that the novel JTFDF feature-based classification model, MSSDM-SqueezeNet-JTFDF, achieved the highest classification performance against all other existing state-of-the-art methods in IMS recognition. Our introduced EEG–BCI models effectively enhance IMS–EEG patterns across multichannel data and offer great potential for the practical deployment of BCI technologies.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"347-357"},"PeriodicalIF":3.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492266","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