@MASTERSTHESIS\{IMM2012-06317,
    author       = "H. Henriksen",
    title        = "A Generative Approach to {EEG} Source Separation, Classification and Artifact Correction",
    year         = "2012",
    school       = "Technical University of Denmark, {DTU} Informatics, {E-}mail: reception@imm.dtu.dk",
    address      = "Asmussens Alle, Building 305, {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    note         = "Supervised by Associate Professor Ole Winther, owi@imm.dtu.dk, {DTU} Informatics",
    url          = "http://www.imm.dtu.dk/English.aspx",
    abstract     = "This thesis deals with the detection of right and left hand-pull stimuli in {EEG} data for five healthy subjects. This paradigm give rise to activation of motor cortex contra-lateral to stimuli side.
{ICA} components obtained from a Kalman filter based algorithm have been applied as features in the classification task and compared with time series features and Infomax {ICA} features. The Kalman {ICA} components have proven to be well-suited for separating the two classes in this thesis, and the Kalman features accomplished the lowest error rates when classifying left and right stimuli. Different classifiers have been tested on the three feature types, and the advanced {SVM} classifier performed best in all cases. The percentage of significant different features between the two classes showed to be strongly correlated to the classification performance. For the purpose of stimuli detection a visual inspection of the {ICA} components has been made. The visible distinction is not as pronounced as the difference in classification performance for the two {ICA} features."
}