Evolutionary Design of a Brain-Computer Interface
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 electroencephalo...
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| creator | Romero, G. Arenas, M. G. Castillo, P. A. Merelo, J. J. |
| description | 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. |
| doi_str_mv | 10.1007/11494669_82 |
| format | Conference Proceeding |
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Experimental results produceMLPs with a classification ability better than those in the literature.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Brain Computer Interfacing</subject><subject>Computer science; control theory; systems</subject><subject>Evolutionary Design</subject><subject>Exact sciences and technology</subject><subject>Learning and adaptive systems</subject><subject>Minimal Expert</subject><subject>Slow Cortical Potential</subject><subject>Training Test</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540262083</isbn><isbn>9783540262084</isbn><isbn>9783540321064</isbn><isbn>3540321063</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNUD1PwzAUNF8SoWTiD2RhYAi8Z7849gilQKVKLDBbTmxXgTSJ4haJf99UZeCGu-FOp9MxdoNwjwDlAyJpklIbxU9YqkslCgLBESSdsgQlYi4E6TN2dTC45KDEOUtAAM91SeKSpTF-wQSBslSYMFz89O1u2_SdHX-zZx-bdZf1IbPZ02ibLp_3m2G39WO27CYOtvbX7CLYNvr0T2fs82XxMX_LV--vy_njKh846m0eyDteyICWQnDkuHNKV4UuoYJKOlc6z4N0QSAJhUFpjZUjIC5rKACVmLHbY-9gY23bMNqubqIZxmYzTTUolSalaMrdHXNxsrq1H03V99_RIJjDZ-bfZ2IPO0JXng</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Romero, G.</creator><creator>Arenas, M. 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J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-f4ed256f1a4ffd4d2dd89b5970b0b6dd7de2f6df314381f8991bd40426c050183</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Brain Computer Interfacing</topic><topic>Computer science; control theory; systems</topic><topic>Evolutionary Design</topic><topic>Exact sciences and technology</topic><topic>Learning and adaptive systems</topic><topic>Minimal Expert</topic><topic>Slow Cortical Potential</topic><topic>Training Test</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Romero, G.</creatorcontrib><creatorcontrib>Arenas, M. G.</creatorcontrib><creatorcontrib>Castillo, P. A.</creatorcontrib><creatorcontrib>Merelo, J. 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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.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11494669_82</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Computational Intelligence and Bioinspired Systems, 2005, p.669-676 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16894884 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Brain Computer Interfacing Computer science control theory systems Evolutionary Design Exact sciences and technology Learning and adaptive systems Minimal Expert Slow Cortical Potential Training Test |
| title | Evolutionary Design of a Brain-Computer Interface |
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