A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm Article Swipe
Rui Li
,
Di Liu
,
Zhijun Li
,
Jinli Liu
,
Jincao Zhou
,
Weiping Liu
,
Bo Liu
,
Weiping Fu
,
Ahmad Bala Alhassan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.988535
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.988535
Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.
Related Topics
Concepts
Brain–computer interface
Computer science
Convolutional neural network
Hyperparameter
Artificial intelligence
Genetic algorithm
Pattern recognition (psychology)
Field (mathematics)
Support vector machine
Electroencephalography
Interface (matter)
Algorithm
Decoding methods
Machine learning
Mathematics
Parallel computing
Pure mathematics
Maximum bubble pressure method
Bubble
Psychiatry
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2022.988535
- https://www.frontiersin.org/articles/10.3389/fnins.2022.988535/pdf
- OA Status
- gold
- Cited By
- 9
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296588595
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4296588595Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2022.988535Digital Object Identifier
- Title
-
A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-13Full publication date if available
- Authors
-
Rui Li, Di Liu, Zhijun Li, Jinli Liu, Jincao Zhou, Weiping Liu, Bo Liu, Weiping Fu, Ahmad Bala AlhassanList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2022.988535Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.988535/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.988535/pdfDirect OA link when available
- Concepts
-
Brain–computer interface, Computer science, Convolutional neural network, Hyperparameter, Artificial intelligence, Genetic algorithm, Pattern recognition (psychology), Field (mathematics), Support vector machine, Electroencephalography, Interface (matter), Algorithm, Decoding methods, Machine learning, Mathematics, Parallel computing, Pure mathematics, Maximum bubble pressure method, Bubble, Psychiatry, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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