Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning

Multimodal physiological signals play a pivotal role in drivers’ perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal p...

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Bibliographic Details
Main Authors: Pengbo Zhao, Cheng Lian, Bingrong Xu, Yixin Su, Zhigang Zeng
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Multimodal physiological signals
self-supervised learning
multiscale entropy
multimodal uncertainty-aware
multimodal cascaded attention
Online Access:https://ieeexplore.ieee.org/document/10551873/
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