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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 6/2013

01.08.2013 | Original Paper

Mind reading with regularized multinomial logistic regression

verfasst von: Heikki Huttunen, Tapio Manninen, Jukka-Pekka Kauppi, Jussi Tohka

Erschienen in: Machine Vision and Applications | Ausgabe 6/2013

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Abstract

In this paper, we consider the problem of multinomial classification of magnetoencephalography (MEG) data. The proposed method participated in the MEG mind reading competition of ICANN’11 conference, where the goal was to train a classifier for predicting the movie the test person was shown. Our approach was the best among ten submissions, reaching accuracy of 68 % of correct classifications in this five category problem. The method is based on a regularized logistic regression model, whose efficient feature selection is critical for cases with more measurements than samples. Moreover, a special attention is paid to the estimation of the generalization error in order to avoid overfitting to the training data. Here, in addition to describing our competition entry in detail, we report selected additional experiments, which question the usefulness of complex feature extraction procedures and the basic frequency decomposition of MEG signal for this application.
Vorheriger Artikel Pi-Tag: a fast image-space marker design based on projective invariants

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Fußnoten
3
Note, that the challenge report [25] erroneously states the frequency features to be the envelopes of the frequency bands. However, the data consists of the plain frequency bands; see the erratum at http://​www.​cis.​hut.​fi/​icann2011/​meg/​megicann_​erratum.​pdf.
 
6
In the subsequent sections we refer to the first-day data as training data, the 25 training samples from the second day as validation data and the remaining 25 samples from the second day as test data. The 653 originally unlabeled test samples from the second day are called secret test data.
 
7
Note, that this is relevant although the stimuli were presented without audio: language processing is not limited to the processing of spoken language [33].
 
8
While short term clips from movie categories 1, 2 and 3 (see Sect. 2.1) were shown by the organizers in an intermingled fashion, the “storyline” movies (categories 4 and 5), have been presented in one continuous block, each at the end of the experiment [25]. Therefore, the acquired signals in categories 1, 2, and 3 might be different to the signals in categories 4 and 5 purely for ‘chronological’ reasons, e.g., decreasing vigilance.
 
9
The term filter (see Guyon and Elisseeff [11]) here refers to the application of a feature selection method that is independent of the classifier.
 
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Metadaten
Titel
Mind reading with regularized multinomial logistic regression
verfasst von
Heikki Huttunen
Tapio Manninen
Jukka-Pekka Kauppi
Jussi Tohka
Publikationsdatum
01.08.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 6/2013
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-012-0464-y

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