Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment-Reference-Cited by-同舟云学术

Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment

Published:2023-10 Issue:10 Volume:53 Page:6426-6437
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Lee Dae-Hyeok¹^ORCID,Jeong Ji-Hoon²^ORCID,Yu Baek-Woon¹,Kam Tae-Eui³^ORCID,Lee Seong-Whan³^ORCID

Affiliation:

1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

2. School of Computer Science, Chungbuk National University, Cheongju, Chungbuk, Republic of Korea

3. Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government [MSIT, Artificial Intelligence Graduate School Program (Korea University)]

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221021/10252086/10159269.pdf?arnumber=10159269

Reference58 articles.

1. A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback

2. Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of Pilots' Brains

3. Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

4. Multimodal Vigilance Estimation Using Deep Learning

5. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness

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