Name
Title | ||
|---|---|---|
Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classification |
Abstract | ||
|---|---|---|
Brain computer interfaces (BCIs) allow the direct human-computer interaction without the need of motor intervention. To properly and efficiently decode brain signals into computer commands the application of machine-learning techniques is required. Evolutionary algorithms have been increasingly applied in different steps of BCI implementations. In this paper we introduce the use of the covariance matrix adaptation evolution strategy (CMA-ES) for BCI systems based on motor imagery. The optimization algorithm is used to evolve linear classifiers able to outperform other traditional classifiers. We also analyze the role of modeling variables interactions for additional insight in the understanding of the BCI paradigms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330163.2330323 | GECCO |
Keywords | Field | DocType |
eeg-based motor imagery classification,covariance matrix adaptation evolution,brain computer interface,bci system,additional insight,model-based evolutionary algorithm,evolutionary algorithm,bci implementation,motor intervention,bci paradigm,decode brain signal,motor imagery,optimization,human computer interaction,machine learning,cma es,evolution strategy,covariance matrix adaptation | Evolutionary algorithm,Computer science,Brain–computer interface,Implementation,Evolution strategy,Optimization algorithm,CMA-ES,Artificial intelligence,Machine learning,Electroencephalography,Motor imagery | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Santana | 1 | 357 | 19.04 |
| Laurent Bonnet | 2 | 23 | 2.28 |
| Jozef Legény | 3 | 13 | 1.40 |
| Lecuyer, Anatole | 4 | 1510 | 142.42 |