- https://doi.org/10.4018/ijsir.302607
Particle Rider Optimization-Driven Classification for Brain-Computer Interface
- Jul 13, 2022
- International Journal of Swarm Intelligence Research
- Megha M Wankhade +1 more
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).
- # Rider Optimization Algorithm
- # Common Average Referencing
- # Brain–computer Interface
- # Hybrid Optimization Algorithm
- # Removal Of Artefact
- # Laplacian Filters
- # Principle Component Analysis
- # Optimization Algorithm
- # Particle Swarm Optimization
- # Control Signals
