EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery

2005 ◽  
Author(s):  
A. Akrami ◽  
S. Solhjoo ◽  
A. Motie-Nasrabadi ◽  
M.-R. Hashemi-Golpayegani
Keyword(s):  
Mental Task ◽  
Nonlinear Classification ◽  
Movement Imagery ◽  
Task Classification ◽  
Linear And Nonlinear

Classification of Activated Sludge Settleability Using Linear and Nonlinear Classification Functions

2008 ◽  
Vol 41 (2) ◽  
pp. 14600-14605
Author(s):  
Geert Gins ◽  
Jef Vanlaer ◽  
Ilse Y. Smets ◽  
Jan F. Van Impe
Keyword(s):  
Activated Sludge ◽  
Nonlinear Classification ◽  
Linear And Nonlinear ◽  
Sludge Settleability

scholarly journals Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results

2012 ◽  
Vol 1 (1) ◽  
pp. 55 ◽  
Author(s):  
Renato Amorim ◽  
Boris Mirkin ◽  
John Q. Gan
Keyword(s):  
Power Spectrum Density ◽  
Independent Learning ◽  
Signal Classification ◽  
Eeg Signal ◽  
Mental Task ◽  
Eeg Signals ◽  
Task Classification ◽  
Spectrum Density ◽  
Anomalous Pattern

In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.

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