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- W2187029095 abstract "Thispaperaddresses theproblemof classification of electroencephalogram (EEG)signals obtained fromhumansubjects performing twomental tasks. Onetasknamedbaseline involves relaxing andthinking of nothing inparticular andtheother tasknamedmultiplication involves mentally multiplying two2-digit integers. First, the EEG signals are pre-processed usingIndependent Component Analysis forremoval ofartifacts. Then, atime- frequency representation ofthesignals isgenerated, from whichwavelet-based texturefeatures areextracted for classification. Thetexture features arefedinto athree-layer neural networkclassifier trained bythebackpropagation algorithm. A classification rateof96% isobtained forthe dataset examined. Theentire classification systemhasbeen implemented intheLabVIEW graphical programming environment providing auser-friendly interface toalter and monitor various parameters oftheneural network classifier." @default.
- W2187029095 created "2016-06-24" @default.
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- W2187029095 date "2005-01-01" @default.
- W2187029095 modified "2023-09-26" @default.
- W2187029095 title "Neuralnetwork classification of BEG signals usingtime- equencyrepresentation" @default.
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- W2187029095 hasPublicationYear "2005" @default.
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