Improving BCI performance by task-related trial pruning

Claudia Sannelli, Mikio Braun, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Noise in electroencephalography data (EEG) is an ubiquitous problem that limits the performance of brain computer interfaces (BCI). While typical EEG artifacts are usually removed by trial rejection or by filtering, noise induced in the data by the subject's failure to produce the required mental state is very harmful. Such "noise" effects are rather common, especially for naive subjects in their training phase and, thus, standard artifact removal methods would inevitably fail. In this paper, we present a novel method which aims to detect such defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space. In this manner, our method effectively "cleans" the training data and thus allows better BCI classification. Preliminary results conducted on a data set of 43 naive subjects show a significant improvement for 74% of the subjects.

Original languageEnglish
Pages (from-to)1295-1304
Number of pages10
JournalNeural Networks
Issue number9
Publication statusPublished - 2009 Nov
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by the DFG (Deutsche Forschungsgemeinschaft) MU 987/3-1.

Copyright 2010 Elsevier B.V., All rights reserved.


  • Artifact Correction/Rejection
  • Brain-Computer Interface
  • Common Spatial Patterns
  • Denoising
  • Kernel Principal Component Analysis

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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