PropertyValue
?:abstract
  • This paper discusses three common strategies to incorporate temporal dynamics of brain activity to recognize 3 mental tasks from spontaneous EEG signals. The networks have been tested in a hard experimental setup; namely, generalization over different recording sessions while analyzing short time windows. It turns out that the simple local neural classifier currently embedded in our BCI, which averages the response to 8 consecutive EEG samples, is to be preferred to more complex time-processing networks such as TDNN and Elman-like. With this local classifier, users with some hours of training are able to operate several brain-actuated applications. ()
?:appearsInConferenceSeries
?:bookTitle
  • ICANN ()
?:citationCount
  • 8 ()
is ?:cites of
?:cites
?:created
  • 2016-06-24 ()
?:creator
?:doi
  • 10.1007/3-540-46084-5_182 ()
?:endingPage
  • 1130 ()
?:estimatedCitationCount
  • 8 ()
is ?:hasCitedEntity of
is ?:hasCitingEntity of
?:hasDiscipline
?:hasURL
?:language
  • en ()
?:publicationDate
  • 2002-08-28 ()
?:publisher
  • Springer, Berlin, Heidelberg ()
?:rank
  • 20384 ()
?:referenceCount
  • 10 ()
?:startingPage
  • 1125 ()
?:title
  • Temporal Processing of Brain Activity for the Recognition of EEG Patterns ()
?:type

Metadata

Anon_0  
expand all