Using Multimodal Methods and Machine Learning to Recognize Mental Workload: Distinguishing Between Underload, Moderate Load, and Overload

Jiang, Zebin; Li, Xinyan; Ge, Liezhong; Xu, Jie; Lu, Yandi; Zhang, Yijing; Mao, Ming

doi:10.1080/10447318.2024.2352936

International Journal of Human–Computer Interaction

2024

DOI: 10.1080/10447318.2024.2352936

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Using Multimodal Methods and Machine Learning to Recognize Mental Workload: Distinguishing Between Underload, Moderate Load, and Overload

Zebin Jiang,

Xinyan Li,

Liezhong Ge

et al.

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Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

Zhang,

Luo,

Cao

et al. 2024

International Journal of Human–Computer Interaction

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Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

Zhang,

Luo,

Cao

et al. 2024

International Journal of Human–Computer Interaction

View full text Add to dashboard Cite

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Using Multimodal Methods and Machine Learning to Recognize Mental Workload: Distinguishing Between Underload, Moderate Load, and Overload

Cited by 1 publication

References 74 publications

Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

Evaluating Pilot Mental Workload Using fNIRS-Based Functional Connectivity Features with a Deep Residual Shrinkage Network Under Emergency Flight Scenarios

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