SADNet: sustained attention decoding in a driving task by self-attention convolutional neural network

SADNet: sustained attention decoding in a driving task by self-attention convolutional neural network

Aim The lack of concentration is one of the primary causes of traffic accidents. Decoding attention states provides a way to monitor drivers' attention, thereby preventing tragedies. However, existing methods for decoding attention states from EEG signals face challenges such as insufficient fe...

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Bibliographic Details
Main Authors: Shuzhong Lai, Lin Yao, Yueming Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Brain-Apparatus Communication
Subjects:
Attention state decoding
self-attention
driving fatigue
deep learning
Online Access:https://www.tandfonline.com/doi/10.1080/27706710.2024.2400063
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https://www.tandfonline.com/doi/10.1080/27706710.2024.2400063

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