Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation-Reference-Cited by-同舟云学术

Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation

Published:2022 Issue: Volume:3 Page:171-177
ISSN:2644-1276
Container-title:IEEE Open Journal of Engineering in Medicine and Biology
language:
Short-container-title:IEEE Open J. Eng. Med. Biol.

Author:

Alwasiti Haider¹^ORCID,Yusoff Mohd Zuki²^ORCID

Affiliation:

1. Helsinki Lab of Interdisciplinary Conservation Science, University of Helsinki, Helsinki, Finland

2. Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

Funder

Ministry of Higher Education, Malaysia

Universiti Teknologi PETRONAS (UTP), Malaysia

Yayasan Universiti Teknologi PETRONAS (YUTP) Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/8782705/9698118/09962758.pdf?arnumber=9962758

Reference40 articles.

1. A novel deep learning approach for classification of EEG motor imagery signals

2. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band

3. Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions

4. Brain-Computer Interfaces

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