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EEG Signal Processing and Feature Extraction pp 335–375Cite as

Statistical Analysis

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Abstract

Statistics is a collection of methods that you can get answers to important questions from EEG signal analysis. In this chapter, we firstly introduced the descriptive statistical methods for presenting the result from the raw data. Furthermore, analysis techniques comprised of parametric strategies like t-test, ANOVA, regression, and nonparametric procedures, such as permutation test, are introduced with their implementation in MATLAB and SPSS. Selecting the right statistical procedures would be important in EEG signal analysis. Finally, we discussed the problem of multiple comparison, which may increase the probability for the researcher to get to false positive conclusion due to the multiple comparison and remains a challenge in statistical method.

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Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Gan Huang

Authors
  1. Gan Huang
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Editor information

Editors and Affiliations

  1. CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Li Hu

  2. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, China

    Zhiguo Zhang

17.1 Supplementary Electronic Material (S)

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© 2019 Springer Nature Singapore Pte Ltd.

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Huang, G. (2019). Statistical Analysis. In: Hu, L., Zhang, Z. (eds) EEG Signal Processing and Feature Extraction. Springer, Singapore. https://doi.org/10.1007/978-981-13-9113-2_17

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