MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification-Reference-Cited by-同舟云学术

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MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification

Published:2024-09-01 Issue:17 Volume:11 Page:28539-28554
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Autthasan Phairot¹^ORCID,Chaisaen Rattanaphon¹^ORCID,Phan Huy²^ORCID,Vos Maarten De³^ORCID,Wilaiprasitporn Theerawit¹^ORCID

Affiliation:

1. Bio-Inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand

2. Amazon AGI, Cambridge, MA, USA

3. Department of Engineering, Department of Development and Regeneration, KU Leuven, Leuven, Belgium

Funder

PTT Public Company Ltd.

SCB Public Company Ltd., Thailand

National Research Council of Thailand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10643444/10533256.pdf?arnumber=10533256

Reference59 articles.

1. Brain–computer interfaces for communication and control

2. Real-time detection of brain events in EEG

3. Using EEG alpha rhythm to control a mobile robot

4. A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

5. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation

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