Predicting EEG Sample Size Required for Classification Calibration

  • Zijing Mao
  • Tzyy-Ping Jung
  • Chin-Teng Lin
  • Yufei HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


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 Since few algorithms are yet available to predict performance for BCIs, our algorithm will be an important tool for real-life BCI applications.


Sample size prediction Calibration Brain computer interface EEG Rapid serial visual presentation Driver’s fatigue 



Research was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2-0022. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implies, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for the Government purposes notwithstanding any copyright notation herein. This work received computational support from Computational System Biology Core at the University of Texas at San Antonio, funded by the National Institute on Minority Health and Health Disparities (G12MD007591) from the National Institutes of Health.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zijing Mao
    • 1
  • Tzyy-Ping Jung
    • 2
  • Chin-Teng Lin
    • 3
  • Yufei Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of TexasSan AntonioUSA
  2. 2.Institute for Neural ComputationUniversity of CaliforniaSan DiegoUSA
  3. 3.Brain Research CenterNational Chiao Tung UniversityHsinchuTaiwan

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