Spatio-spectral filters for improving the classification of single trial EEG

Steven Lemm, Benjamin Blankertz, Gabriel Curio, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

508 Citations (Scopus)


Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.

Original languageEnglish
Pages (from-to)1541-1548
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number9
Publication statusPublished - 2005 Sept


  • BCI
  • CSP
  • Classification
  • Feature extraction

ASJC Scopus subject areas

  • Biomedical Engineering


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