EEG-Based Classification of Cognitive Load and Task Conditions for AR Supported Construction Assembly: A Deep Learning Approach-Reference-Cited by-同舟云学术

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EEG-Based Classification of Cognitive Load and Task Conditions for AR Supported Construction Assembly: A Deep Learning Approach

Published:2024-01-25 Issue: Volume:16 Page:248-256
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Qin Yimin¹²³,Bulbul Tanyel¹²³,Withers Jeremy¹²³

Affiliation:

1. Ph.D. Candidate, Myers Lawson School of Construction, Virginia Tech, Blacksburg, VA.

2. Associate Professor, Myers Lawson School of Construction, Virginia Tech, Blacksburg, VA.

3. Ph.D. Student, Myers Lawson School of Construction, Virginia Tech, Blacksburg, VA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485231.030

Reference21 articles.

1. Amiga A. 2021. “HoloLens Industry Summit.” Accessed March 18 2021. microsoftevent.eventbuilder.com/event/36217/occurrence/33851/recording?rauth=75.2421043.d7c6548f0845680f8e306e4772c23a05bf2932a6006ced74a65c598952395cbf.

2. Ballas N. L. Yao C. Pal and A. Courville. 2015. “Delving Deeper into Convolutional Networks for Learning Video Representations.” arXiv.org. Accessed March 6 2023. https://arxiv.org/abs/1511.06432v4.

3. Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment

4. Cheng B. C. Fan H. Fu J. Huang H. Chen and X. Luo. 2022. “Measuring and computing cognitive statuses of construction workers based on electroencephalogram: a critical review.” IEEE Trans. Comput. Soc. Syst. IEEE.

5. Deep learning for electroencephalogram (EEG) classification tasks: a review;Craik A.;J. Neural Eng,2019

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