Linear and nonlinear methods for brain-computer interfaces

Klaus Robert Müller, Charles W. Anderson, Gary E. Birch

Research output: Contribution to journalArticlepeer-review

223 Citations (Scopus)


At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets.

Original languageEnglish
Pages (from-to)165-169
Number of pages5
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Publication statusPublished - 2003 Jun


  • Feature spaces
  • Fisher's discriminant
  • Linear methods
  • Mathematical programming machines
  • Support vector machines (SVMs)

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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