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Statistical Modeling in Biomedical Research pp 23–35Cite as

Classification of EEG Motion Artifact Signals Using Spatial ICA

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Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

Abstract

Using electroencephalography (EEG) data to extract information about cortical signals has become an increasingly explored task of interest in the field of computational neuroscience. In this paper, we proposed a novel procedure which reduce dimension by applying spatial Independent Component Analysis (SICA) on EEG motion artifact data and classify gait speed for a given subject by the projected EEG motion artifact signals. Whereas most applications of ICA in analyzing EEG data employ temporal ICA, we use SICA and Principal Component Analysis for dimension reduction before applying classifiers such as Support Vector Machines, Naive Bayes, and multinomial logistic regression to the extracted independent components. We evaluate and compare the classification models by using randomly selected channels from the multi-channel EEG motion artifact data as our test data. For practical application and interpretation, we treat the test channels as if they might come from a new trial for the given subject.

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Acknowledgement

We thank NIH for grant 1R01AG054621-01 which partially supported this study.

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Authors and Affiliations

  1. Department of Statistics and Data Science, University of Central Florida, Orlando, FL, USA

    Hsin-Hsiung Huang & Aubrey Condor

  2. Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA

    Helen J. Huang

Authors
  1. Hsin-Hsiung Huang
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  2. Aubrey Condor
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  3. Helen J. Huang
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Corresponding author

Correspondence to Hsin-Hsiung Huang .

Editor information

Editors and Affiliations

  1. Math and Statistics, 1342, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, USA

    Ding-Geng (Din) Chen

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Huang, HH., Condor, A., Huang, H.J. (2020). Classification of EEG Motion Artifact Signals Using Spatial ICA. In: Zhao, Y., Chen, DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-33416-1_2

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