Name
Abstract | ||
|---|---|---|
This paper shows how Evolutionary Algorithm (EA) robustness help to solve a difficult problem with a minimal expert knowledge about it. The problem consist in the design of a Brain-Computer Interface (BCI), which allows a person to communicate without using nerves and muscles. Input electroencephalographic (EEG) activity recorded from the scalp must be translated into outputs that control external devices. Our BCI is based in a Multilayer Perceptron (MLP) trained by an EA. This kind of training avoids the main problem of MLPs training algorithms: overfitting. Experimental results produceMLPs with a classification ability better than those in the literature. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11494669_82 | IWANN |
Keywords | Field | DocType |
mlps training algorithm,brain-computer interface,difficult problem,external device,classification ability,multilayer perceptron,experimental results producemlps,evolutionary algorithm,main problem,input electroencephalographic,evolutionary design,brain computer interface | Evolutionary algorithm,Computer science,Brain–computer interface,Robustness (computer science),Multilayer perceptron,Artificial intelligence,Overfitting,Artificial neural network,Machine learning,Genetic algorithm,Electroencephalography | Conference |
Volume | ISSN | ISBN |
3512 | 0302-9743 | 3-540-26208-3 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Romero | 1 | 81 | 7.38 |
| M. G. Arenas | 2 | 48 | 6.27 |
| P. A. Castillo | 3 | 134 | 13.95 |
| J. J. Merelo | 4 | 363 | 33.51 |