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An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction

Hong Zeng, Xiufeng Li, Gianluca Borghini, Yue Zhao, Pietro Aricò, Gianluca Di Flumeri, Nicolina Sciaraffa, Wael Zakaria, Wanzeng Kong, Fabio Babiloni
2021 Sensors  
Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue.  ...  cross-subject fatigue detection.  ...  Conclusions In this paper, we propose an improved DANN-based transfer learning model, GDANN, and apply it for EEG-based cross-subject fatigue mental state prediction.  ... 
doi:10.3390/s21072369 pmid:33805522 fatcat:syi4spcwqbd5zoeaktnjmujoay

Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface [article]

Dongdong Zhang, Dong Cao, Haibo Chen
2019 arXiv   pre-print
Here in this paper we present a deep learning-based EEG decoding method to read mental states.  ...  However, the mental state prediction accuracy and generality through encephalogram (EEG) are not good enough for everyday use.  ...  ., Ltd. and DeepBlue Academy of Sciences for their support.  ... 
arXiv:1911.05661v1 fatcat:jni3mwji2ralxiwhl3h7esnbvq

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Hong Zeng, Chen Yang, Hua Zhang, Zhenhua Wu, Jiaming Zhang, Guojun Dai, Fabio Babiloni, Wanzeng Kong
2019 Computational Intelligence and Neuroscience  
Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states.  ...  However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge.  ...  Brain-Machine Collaborative Intelligence of Hangzhou Dianzi University, and Industrial Neuroscience Lab of University of Rome "La Sapienza." e authors also thank BrainSigns srl for the support.  ... 
doi:10.1155/2019/3761203 pmid:31611912 pmcid:PMC6755292 fatcat:2kaklxhdmvhy3hd53bpdapwiae

Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation

Yue Zhao, Guojun Dai, Gianluca Borghini, Jiaming Zhang, Xiufeng Li, Zhenyan Zhang, Pietro Aricò, Gianluca Di Flumeri, Fabio Babiloni, Hong Zeng
2021 Frontiers in Human Neuroscience  
In this study, we propose a Label-based Alignment Multi-Source Domain Adaptation (LA-MSDA) for cross-subject EEG fatigue mental state evaluation.  ...  Electroencephalogram (EEG) based methods are proven to be efficient to evaluate mental fatigue.  ...  Overall, the results indicate the effectiveness of LA-MSDA for cross-subject EEG fatigue mental state evaluation.  ... 
doi:10.3389/fnhum.2021.706270 pmid:34658814 pmcid:PMC8519604 fatcat:uiq4se4acvaghcx4j7b7gohk4a

Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Mahsa Bagheri, Sarah D. Power
2022 Sensors  
Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level  ...  Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with  ...  Recently, [48] presented an EEG-based classification of four mental states (fatigue, workload, distraction, and the normal state) for seven pilots in both offline and pseudo-online analyses.  ... 
doi:10.3390/s22020535 pmid:35062495 pmcid:PMC8781201 fatcat:w6lwkyobjjat5o65q2hqioxcg4

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study

Mauricio A. Ramírez-Moreno, Patricio Carrillo-Tijerina, Milton Osiel Candela-Leal, Myriam Alanis-Espinosa, Juan Carlos Tudón-Martínez, Armando Roman-Flores, Ricardo A. Ramírez-Mendoza, Jorge de J. Lozoya-Santos
2021 International Journal of Environmental Research and Public Health  
In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented.  ...  Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband.  ...  Acknowledgments: Authors would like to thank Tecnologico de Monterrey for the support during the development of this project. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijerph182211891 pmid:34831645 pmcid:PMC8621458 fatcat:rhbqhndh7bgwvpak2ydepjf224

Multimodal MRI Analysis of Brain Metabolism in Maintenance Hemodialysis Patients Based on Cognitive Computing

Yan Zhang, Hui Ma, Xinguang Lv, Qinjun Han, Dilbag Singh
2021 Journal of Healthcare Engineering  
The constructed cognitive computing method for cross-individual emotion state recognition achieves optimization and innovation of EEG emotion pattern recognition, which can effectively predict people's  ...  mental emotion state from EEG signals.  ...  the mean and standard deviation of the test results of 30 subjects. e accuracy of the dynamic entropy-based EEG pattern learning method for cross-individual positive and negative emotion recognition was  ... 
doi:10.1155/2021/7231658 pmid:34422245 pmcid:PMC8371624 fatcat:fgkit3dfgbf77l3me5nzitxlru

Transfer Components Between Subjects for EEG-based Driving Fatigue Detection [chapter]

Yong-Qi Zhang, Wei-Long Zheng, Bao-Liang Lu
2015 Lecture Notes in Computer Science  
In this paper, we first build up an electroencephalogram (EEG)-based driving fatigue detection system, and then propose a subject transfer framework for this system via component analysis.  ...  The improvement shows the feasibility and efficiency of our approach for subject transfer driving fatigue detection from EEG.  ...  Sinno Jialin Pan, Nanyang Technological University, Singapore for providing the source code of Tranfer Component Analysis.  ... 
doi:10.1007/978-3-319-26561-2_8 fatcat:i7o7itf5j5cr7fovtmpnahslia

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Hong Zeng, Jiaming Zhang, Wael Zakaria, Fabio Babiloni, Borghini Gianluca, Xiufeng Li, Wanzeng Kong
2020 Sensors  
In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection.  ...  EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications.  ...  Therefore, in this paper, an improved EasyTL-based method, InstanceEasyTL, is proposed for overcoming such shortcoming of EasyTL for cross-subject EEG analysis.  ... 
doi:10.3390/s20247251 pmid:33348823 pmcid:PMC7766235 fatcat:xoaguqgrr5errmuqgt2wfs3m2e

Improving Cross-State and Cross-Subject Visual ERP-based BCI with Temporal Modeling and Adversarial Training

Ziyi Ni, Jiaming Xu, Yuwei Wu, Mengfan Li, Guizhi Xu, Bo Xu
2022 IEEE transactions on neural systems and rehabilitation engineering  
The results indicate that our hierarchical method can effectively model the long sequence EEG raw data, outperform the baselines on most conditions, including cross-state and cross-subject conditions.  ...  Finally, we show how deep learning-based methods with limited EEG data can improve ERP-based BCI with adversarial training. Our code will be released at  ...  For example, training is carried out in the low mental workload state when testing under the high mental workload.  ... 
doi:10.1109/tnsre.2022.3150007 pmid:35133966 fatcat:d3p2zi5rwng75pq46xxamyka2a

E-Key: an EEG-Based Biometric Authentication and Driving Fatigue Detection System

Tao Xu, Hongtao Wang, Guanyong Lu, Feng Wan, Mengqi Deng, Peng Qi, Anastasios Bezerianos, Cuntai Guan, Yu Sun
2021 IEEE Transactions on Affective Computing  
The performance was assessed using EEG data collected through a wearable dry-sensor system from 31 healthy subjects undergoing a 90-min simulated driving task.  ...  Due to the increasing fatal traffic accidents, there are strong desire for more effective and convenient techniques for driving fatigue detection.  ...  New advances in feature constructions and selection [82] - [84] as well as employment of advanced deep learning methods (including transfer learning [83] , adaptive learning [85] , multi-task learning  ... 
doi:10.1109/taffc.2021.3133443 fatcat:uxzkddpixnfcllgsdex4kdfrbi

Mental State Classification Using Multi-graph Features [article]

Guodong Chen and Hayden S. Helm and Kate Lytvynets and Weiwei Yang and Carey E. Priebe
2022 arXiv   pre-print
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive  ...  predictive information.  ...  Acknowledgements We would like to thank Ben Cutler, Steven Dong, Amber Hoak, Tzyy-Ping Jung, Ben Pedigo, Siddharth Siddharth, and Christopher White for helpful comments and suggestions throughout our investigations  ... 
arXiv:2203.00516v1 fatcat:txvsywjtafapdpg55erc2mnxgi

EEG-Based Tool for Prediction of University Students' Cognitive Performance in the Classroom

Mauricio A. Ramírez-Moreno, Mariana Díaz-Padilla, Karla D. Valenzuela-Gómez, Adriana Vargas-Martínez, Juan C. Tudón-Martínez, Rubén Morales-Menendez, Ricardo A. Ramírez-Mendoza, Blas L. Pérez-Henríquez, Jorge de J. Lozoya-Santos
2021 Brain Sciences  
These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks.  ...  This study presents a neuroengineering-based machine learning tool developed to predict students' performance under different learning modalities.  ...  Acknowledgments: The authors would like to thank Tecnologico de Monterrey for the support during the development of this project. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci11060698 pmid:34073242 fatcat:gqrgvylyefhgne4yqmhjtqyjha

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [article]

Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
2020 arXiv   pre-print
Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track  ...  Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI  ...  ACKNOWLEDGMENT Credit authors for icons made from  ... 
arXiv:2001.11337v1 fatcat:cmurfjykjja3rdifr7e7cqq3wy

Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope and Image Processing

Naveen Senniappan Karuppusamy, Bo-Yeong Kang
2020 IEEE Access  
In order to overcome the above limitation we propose a neural network based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing  ...  It was found that the proposed hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver.  ...  Thus Cost(W) gives the cost for predicting driver fatigue based on each epoch.  ... 
doi:10.1109/access.2020.3009226 fatcat:ucxkxekemzckndov2ujer2tvwi
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