Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG - Publication - Bridge of Knowledge

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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG

Abstract

EEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red, green, and blue). Particularly, we assess if a compact and feature-extraction-independent deep learning method (EEGNet) can effectively learn from these EEG responses. Our outcomes outperformed previous works focused on a dataset composed of EEG signals belonging to 7 subjects while seeing and imagining three primary colors. The method reaches an accuracy of 45% for exposed colors, 43% for imagined colors, and 35% for the six classes. Last, the experiments suggest that EEGNet learned to discover patterns in the EEG signals recorded for imagined and exposed colors, and for the six classes, too.

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Details

Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Published in:
LECTURE NOTES IN COMPUTER SCIENCE pages 150 - 160,
ISSN: 0302-9743
Title of issue:
Advances in Computational Intelligence strony 150 - 160
Language:
English
Publication year:
2022
Bibliographic description:
Torres-García A. A., Garcia Salinas J., Villaseñor-Pineda L.: Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG// Advances in Computational Intelligence/ : , 2022, s.150-160
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-031-19493-1_12
Verified by:
Gdańsk University of Technology

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