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

Benjamin Blankertz, Steven Lemm, Matthias Treder, Stefan Haufe, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

864 Citations (Scopus)


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
Issue number2
Publication statusPublished - 2011 May 15

Bibliographical note

Funding Information:
The studies were partly supported by the Bundesministerium für Bildung und Forschung (BMBF) , Fkz 01IB001A/B , 01GQ0850 , by the German Science Foundation (DFG, contract MU 987/3-1 ), by the European Union under the PASCAL2 Network of Excellence, ICT-216886 . This publication only reflects the authors' views. Funding agencies are not liable for any use that may be made of the information contained herein.


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

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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