Mixed effects models for eeg evoked response detection

Yonghong Huang, Deniz Erdogmus, Misha Pavel, Santosh Mathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Human brain signals associated with perceptual processes have been shown to be useful for visual target image search. For the purpose of online training, we develop a novel mixed effects evoked response detector, which is capable of combining individual random effects and population fixed effects, for the analysis of neural signatures associated with targets. To avoid numerical problems in high dimensional matrix computations, we develop equivalent dimension reduced expressions for the mixed models. We construct the mixed effects evoked response model using principal component analysis to provide bases for the population model and linear discriminant analysis (LDA) to provide bases for the individual models. In addition, the LDA is adopted for Elecroencephalography channel dimensionality reduction. Data collected at different time and experimental conditions from two subjects performing image search tasks are utilized to assess the quality of the models. We also compare the proposed model with the support vector machine (SVM). The results demonstrate that the mixed models approach the SVM and provide reliable inference on cross session evaluation for the single-trial evoked response detection.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages91-96
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: Oct 16 2008Oct 19 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period10/16/0810/19/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

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