Source:http://linkedlifedata.com/resource/pubmed/id/15473195
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
3
|
| pubmed:dateCreated |
2004-10-11
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| pubmed:abstractText |
The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.
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| pubmed:grant | |
| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
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| pubmed:month |
Sep
|
| pubmed:issn |
1534-4320
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
12
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
331-8
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| pubmed:dateRevised |
2007-11-14
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| pubmed:meshHeading |
pubmed-meshheading:15473195-Adult,
pubmed-meshheading:15473195-Algorithms,
pubmed-meshheading:15473195-Artificial Intelligence,
pubmed-meshheading:15473195-Brain,
pubmed-meshheading:15473195-Cerebral Palsy,
pubmed-meshheading:15473195-Communication Aids for Disabled,
pubmed-meshheading:15473195-Computer Peripherals,
pubmed-meshheading:15473195-Electroencephalography,
pubmed-meshheading:15473195-Evoked Potentials, Somatosensory,
pubmed-meshheading:15473195-Female,
pubmed-meshheading:15473195-Humans,
pubmed-meshheading:15473195-Male,
pubmed-meshheading:15473195-Middle Aged,
pubmed-meshheading:15473195-Online Systems,
pubmed-meshheading:15473195-Pattern Recognition, Automated,
pubmed-meshheading:15473195-Task Performance and Analysis,
pubmed-meshheading:15473195-Therapy, Computer-Assisted,
pubmed-meshheading:15473195-User-Computer Interface,
pubmed-meshheading:15473195-Word Processing
|
| pubmed:year |
2004
|
| pubmed:articleTitle |
Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).
|
| pubmed:affiliation |
Binuscan, MC-98013 Monaco, Principality of Monaco. georg_fabiani@binuscan.com
|
| pubmed:publicationType |
Journal Article,
Clinical Trial,
Comparative Study,
Research Support, U.S. Gov't, P.H.S.,
Controlled Clinical Trial,
Research Support, Non-U.S. Gov't,
Validation Studies
|