Single-trial analysis and classification of ERP components - A tutorial

Benjamin Blankertz, Steven Lemm, Matthias Treder, Stefan Haufe, Klaus Muller

Research output: Contribution to journalArticle

623 Citations (Scopus)

Abstract

Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.

Original languageEnglish
Pages (from-to)814-825
Number of pages12
JournalNeuroImage
Volume56
Issue number2
DOIs
Publication statusPublished - 2011 May 15
Externally publishedYes

Keywords

  • BCI
  • Decoding
  • EEG
  • ERP
  • LDA
  • Machine learning
  • Shrinkage
  • Spatial filter
  • Spatial pattern

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

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  • Cite this

    Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Muller, K. (2011). Single-trial analysis and classification of ERP components - A tutorial. NeuroImage, 56(2), 814-825. https://doi.org/10.1016/j.neuroimage.2010.06.048