Search
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.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (3)
-
Alejandro A. Torres-García
-
Luis Villaseñor-Pineda
Cite as
Full text
full text is not available in portal
Keywords
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
seen 79 times
Recommended for you
Tensor Decomposition for Imagined Speech Discrimination in EEG
- J. Garcia Salinas,
- L. Villaseñor-Pineda,
- C. A. Reyes-Garćia
- + 1 authors
Decoding imagined speech for EEG-based BCI
- C. A. Reyes-García,
- A. A. Torres-García,
- T. Hernández-del-Toro
- + 2 authors
Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
- J. Garcia Salinas,
- A. A. Torres-García,
- C. A. Reyes-Garćia
- + 1 authors
Transfer learning in imagined speech EEG-based BCIs
- J. Garcia Salinas,
- L. Villaseñor-Pineda,
- C. A. Reyes-Garćia
- + 1 authors