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Pattern analysis of EEG responses to speech and voice: Influence of feature grouping

Pattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external... Full description

Journal Title: NeuroImage 15 February 2012, Vol.59(4), pp.3641-3651
Main Author: Hausfeld, Lars
Other Authors: De Martino, Federico , Bonte, Milene , Formisano, Elia
Format: Electronic Article Electronic Article
Language: English
Subjects:
EEG
Erp
ID: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2011.11.056
Link: https://www.sciencedirect.com/science/article/pii/S1053811911013383
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recordid: elsevier_sdoi_10_1016_j_neuroimage_2011_11_056
title: Pattern analysis of EEG responses to speech and voice: Influence of feature grouping
format: Article
creator:
  • Hausfeld, Lars
  • De Martino, Federico
  • Bonte, Milene
  • Formisano, Elia
subjects:
  • EEG
  • Erp
  • Machine Learning
  • Classification
  • Audition
  • Speech
  • Medicine
ispartof: NeuroImage, 15 February 2012, Vol.59(4), pp.3641-3651
description: Pattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external stimuli or internal brain states. When applied to the analysis of electroencephalography (EEG) or magnetoencephalography (MEG) data, a choice needs to be made on how the input features to the algorithm are obtained from the signal amplitudes measured at the various channels. In this article, we consider six types of pattern analyses deriving from the combination of three types of feature selection in the temporal domain ( , , with two approaches to handle the channel dimension ( , ). We combined these different types of analyses with a Gaussian Naïve Bayes classifier and analyzed a multi-subject...
language: eng
source:
identifier: ISSN: 1053-8119 ; E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2011.11.056
fulltext: fulltext
issn:
  • 1053-8119
  • 10538119
  • 1095-9572
  • 10959572
url: Link


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subjectEEG ; Erp ; Machine Learning ; Classification ; Audition ; Speech ; Medicine
descriptionPattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external stimuli or internal brain states. When applied to the analysis of electroencephalography (EEG) or magnetoencephalography (MEG) data, a choice needs to be made on how the input features to the algorithm are obtained from the signal amplitudes measured at the various channels. In this article, we consider six types of pattern analyses deriving from the combination of three types of feature selection in the temporal domain ( , , with two approaches to handle the channel dimension ( , ). We combined these different types of analyses with a Gaussian Naïve Bayes classifier and analyzed a multi-subject...
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