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

Zeng, Hong; Li, Xiufeng; Borghini, Gianluca; Zhao, Yue; Aricò, Pietro; Flumeri, Gianluca Di; Sciaraffa, Nicolina; Zakaria, Wael; Kong, Wanzeng; Babiloni, Fabio

doi:10.3390/s21072369

Cited by 47 publications

(29 citation statements)

References 35 publications

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“…In addition, EEG of the other three mental states (WUP, PERFO, DROWS) is also recorded without any stimuli but at different driving speeds. Please refer to Zeng et al ( 33 , 34 ), and Zhao et al ( 35 ) for more details.…”

Section: Methodsmentioning

confidence: 99%

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Zeng

Jin

et al. 2022

Front. Psychiatry

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Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

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Section: Methodsmentioning

confidence: 99%

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Zeng

Jin

et al. 2022

Front. Psychiatry

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“…To statistically test whether the interpersonal model significantly outperformed the individual model, we used a two-tailed Wilcoxon signed-rank test, which was also used in [ 80 ]. As shown in Figure 8 , we pooled the accuracy and f1-score values for all the modalities and emotional dimensions to compare them between the interpersonal model and individual model.…”

Section: Methodsmentioning

confidence: 99%

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication

Quan

Miyake

Nozawa

2021

Sensors

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During social interaction, humans recognize others’ emotions via individual features and interpersonal features. However, most previous automatic emotion recognition techniques only used individual features—they have not tested the importance of interpersonal features. In the present study, we asked whether interpersonal features, especially time-lagged synchronization features, are beneficial to the performance of automatic emotion recognition techniques. We explored this question in the main experiment (speaker-dependent emotion recognition) and supplementary experiment (speaker-independent emotion recognition) by building an individual framework and interpersonal framework in visual, audio, and cross-modality, respectively. Our main experiment results showed that the interpersonal framework outperformed the individual framework in every modality. Our supplementary experiment showed—even for unknown communication pairs—that the interpersonal framework led to a better performance. Therefore, we concluded that interpersonal features are useful to boost the performance of automatic emotion recognition tasks. We hope to raise attention to interpersonal features in this study.

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“…In (23), the authors used a flight simulator to construct a simulation environment, induce different mental states, and collect biological signals. In (24), a generative domain-adversarial neural network-based model was presented to solve the problem of a different distribution of EEG. In (25), a driving fatigue detection method based on partially oriented coherent graph CNN is proposed.…”

Section: Related Studymentioning

confidence: 99%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

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The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.

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

Cited by 47 publications

References 35 publications

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

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