On robust parameter estimation in brain-computer interfacing

Wojciech Samek, Shinichi Nakajima, Motoaki Kawanabe, Klaus Robert Müller

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)

Abstract

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

Original languageEnglish
Article number061001
JournalJournal of Neural Engineering
Volume14
Issue number6
DOIs
Publication statusPublished - 2017 Nov 23

Bibliographical note

Funding Information:
This work was supported by the Brain Korea 21 Plus Program through the National Research Foundation of Korea funded by the Ministry of Education. The Institute for Information & Communications Technology Promotion (IITP) grant, funded by the Korean government (No. 2017-0-00451), supported this work. KRM gratefully acknowledges financial support from DFG (DFG SPP 1527, MU 987/14-1) and BMBF (BBDC). This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein. Correspondence to WS and KRM.

Funding Information:
This work was supported by the Brain Korea 21 Plus Program through the National Research Foundation of Korea funded by the Ministry of Education. The Institute for Information & Communications Technology Promotion (IITP) grant, funded by the Korean government (No. 2017-0-00451), supported this work. KRM gratefully acknowledges financial support from DFG (DFG SPP 1527, MU 987/14-1) and BMBF (BBDC).

Publisher Copyright:
© 2017 IOP Publishing Ltd.

Keywords

  • brain-computer interfacing
  • common spatial patterns
  • parameter estimation
  • robustness

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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