Feature Extraction for a Genetic Programming-Based Brain-Computer Interface
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{souza:2022:IS,
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author = "Gabriel Henrique {de Souza} and
Gabriel Oliveira Faria and Luciana Paixao Motta and
Heder Soares Bernardino and Alex Borges Vieira",
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title = "Feature Extraction for a Genetic {Programming-Based}
{Brain-Computer} Interface",
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booktitle = "Brazilian Conference on Intelligent Systems, BRACIS
2022, part 1",
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year = "2022",
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editor = "Joao Carlos Xavier-Junior and Ricardo Araujo Rios",
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volume = "13653",
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series = "Lecture Notes in Computer Science",
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pages = "135--149",
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address = "Campinas, Brazil",
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month = nov # " 28-" # dec # " 1",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-031-21686-2",
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URL = "http://link.springer.com/chapter/10.1007/978-3-031-21686-2_10",
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DOI = "doi:10.1007/978-3-031-21686-2_10",
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abstract = "Brain-Computer Interfaces (BCI) open a two-way
communication channel between a computer and the brain:
while the brain can control the computer, the computer
can induce changes in the brain through feedback. This
mechanism is used in post-stroke motor rehabilitation,
in which a BCI provides feedback by classifying signals
collected from a patient brain. Single Feature Genetic
Programming (SFGP) can create classifiers for these
signals. However, the Genetic Programming (GP) step in
SFGP requires a set of extracted features to generate
its model. To the best of our knowledge, the LogPower
function is the only initial feature extraction
function used in SFGP. Nevertheless, other functions
can improve the quality of the generated classifiers.
Thus, we analyze new initial feature extraction
functions for GP in SFGP. We test the Common Spatial
Patterns, Nonlinear Energy, Average Power Spectral
Density, and Curve Length methods on two datasets
suitable for post-stroke rehabilitation training. The
results obtained show that the analyzed functions
outperform LogPower in all our experiments, with a
kappa value up to 25.20 percent better. We further test
the proposed methods on a third dataset, created with
low-cost equipment. In this case, we show that the
Average Power Spectral Density function outperforms
LogPower by 11.39 percent when three electrodes are
used. Thus, we demonstrate that the new approach can be
used with low-cost equipment and a small number of
electrodes, reducing the financial costs of treatment
and improving patients' comfort.",
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notes = "BRACIS 2022",
- }
Genetic Programming entries for
Gabriel Henrique de Souza
Gabriel Oliveira Faria
Luciana Paixao Motta
Heder Soares Bernardino
Alex Borges Vieira
Citations