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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Computer Modeling in Engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Cross-Subject emotion recognition from EEG using Convolutional Neural Networks

Cross-Subject emotion recognition from EEG using Convolution...

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第三十九届中国控制会议

作者： Xiaolong Zhong Zhong Yin Jianhua Zhang Engineering Research Center of Optical Instrument and System Ministry of EducationShanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and Technology

Using electroencephalogram（EEG） signals for emotion detection has aroused wide spread research ***,across subjects emotional recognition has become an insurmountable gap which researchers cannot step across for a long time due to the poor generalizability of features across subj *** response to this difficulty,in this study,the moving average（MA）technology is introduced to smooth out short-term fluctuations and highlight longer-term trends or *** on the MA technology,an effective method for cross-subject emotion recognition was then developed,which designed a method of salient region extraction based on attention mechanism, with the purpose of enhancing the capability of representations generated by a network by modelling the interdependencies between the channels of its informative *** effectiveness of our method was validated on a dataset for emotion analysis using physiological signals（DEAP） and the MAHNOB-HCI multimodal tagging *** with recent similar works,the method developed in this study for emotion recognition across all subjects was found to be effective,and its accuracy was 66.23% for valence and 68.50% for arousal（DEAP） and 70.25% for valence and73.27% for arousal（MAHNOB） on the Gamma frequency *** benefiting from the strong representational learning capacity in the two-dimensional space,our method is efficient in emotion recognition especially on Beta and Gamma waves.

关键词： emotional recognition Physiological signals Deep learning Classification Machine learning Cross-subject

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