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Particle Rider Optimization-Driven Classification for Brain-Computer Interface

Particle Rider Optimization-Driven Classification for Brain-Computer Interface

Megha M. Wankhade, Suvarna S. Chorage
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 25
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781683181514|DOI: 10.4018/IJSIR.302607
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MLA

Wankhade, Megha M., and Suvarna S. Chorage. "Particle Rider Optimization-Driven Classification for Brain-Computer Interface." IJSIR vol.13, no.1 2022: pp.1-25. http://doi.org/10.4018/IJSIR.302607

APA

Wankhade, M. M. & Chorage, S. S. (2022). Particle Rider Optimization-Driven Classification for Brain-Computer Interface. International Journal of Swarm Intelligence Research (IJSIR), 13(1), 1-25. http://doi.org/10.4018/IJSIR.302607

Chicago

Wankhade, Megha M., and Suvarna S. Chorage. "Particle Rider Optimization-Driven Classification for Brain-Computer Interface," International Journal of Swarm Intelligence Research (IJSIR) 13, no.1: 1-25. http://doi.org/10.4018/IJSIR.302607

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Abstract

The emerging technology for translating the intention of human into control signals is the Brain–computer interface (BCI). The BCI helps the patients with complete motor dysfunction to interact with the people. In this research, a method for abnormality assessment in humans from the perspective of the BCI was proposed by developing a hybrid optimization algorithm based on Electroencephalography (EEG). The hybrid optimization algorithm, called Particle Rider Optimization Algorithm (PROA) is designed through the incorporation of Particle Swarm Optimization (PSO) and Rider Optimization algorithm (ROA). The pre-processing is done for filtering the noise and removal of artefact. In pre-processing, the noise is removed through the Common Average Referencing (CAR) and Laplacian filters, whereas the artifacts are eliminated by Principle component analysis (PCA).

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