35 search results - page 1 / 7 » Classifying n-back EEG data using entropy and mutual informa... |
ESANN
2007
13 years 6 months ago
2007
In this work we show that entropy (H) and mutual information (MI) can be used as methods for extracting spatially localized features for classification purposes. In order to incre...
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Voted
ICANN
2007
Springer
13 years 8 months ago
2007
Springer
Abstract. In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information ...
GRC
2008
IEEE
13 years 5 months ago
2008
IEEE
Feature selection is an important problem for pattern classification systems. Mutual information is a good indicator of relevance between variables, and has been used as a measure...
KES
2008
Springer
13 years 4 months ago
2008
Springer
Epilepsy is one of the most frequent neurological disorders. The main method used in epilepsy diagnosis is electroencephalogram (EEG) signal analysis. However this method requires ...
ICML
2000
IEEE
13 years 9 months ago
2000
IEEE
We present feature transformations useful for exploratory data analysis or for pattern recognition. Transformations are learned from example data sets by maximizing the mutual inf...