Predicting EEG Sample Size Required for Classification Calibration

Zijing Mao, Tzyy Ping Jung, Chin-Teng Lin, Yufei Huang

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

1 Scopus citations

Abstract

This study considers an important problem of predicting required calibration sample size for electroencephalogram (EEG)-based classification in brain computer interaction (BCI). We propose an adaptive algorithm based on learning curve fitting to learn the relationship between sample size and classification performance for each individual subject. The algorithm can always provide the predicted result in advance of reaching the baseline performance with an average error of 17.4 %. By comparing the learning curve of different classifiers, the algorithm can also recommend the best classifier for a BCI application. The algorithm also learns a sample size upper bound from the prior datasets and uses it to detect subject outliers that potentially need excessive amount of calibration data. The algorithm is applied to three EEG-based BCI datasets to demonstrate its utility and efficacy. A Matlab package with GUI is also developed and available for downloading at https://github.com/ZijingMao/LearningCurveFittingForSampleSizePrediction. Since few algorithms are yet available to predict performance for BCIs, our algorithm will be an important tool for real-life BCI applications.
Original languageEnglish
Title of host publicationFOUNDATIONS OF AUGMENTED COGNITION: NEUROERGONOMICS AND OPERATIONAL NEUROSCIENCE
PublisherSpringer-Verlag Berlin Heidelberg
Pages57-68
Number of pages12
ISBN (Print)978-3-319-39955-3, 978-3-319-39954-6
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Sample size prediction
  • Calibration
  • Brain computer interface; EEG; Rapid serial visual presentation
  • Driver's fatigue

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  • Cite this

    Mao, Z., Jung, T. P., Lin, C-T., & Huang, Y. (2016). Predicting EEG Sample Size Required for Classification Calibration. In FOUNDATIONS OF AUGMENTED COGNITION: NEUROERGONOMICS AND OPERATIONAL NEUROSCIENCE (pp. 57-68). (Lecture Notes in Artificial Intelligence). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-39955-3_6