Toward unsupervised adaptation of LDA for brain-computer interfaces

C. Vidaurre, M. Kawanabe, P. Von Bünau, B. Blankertz, K. R. Müller

Research output: Contribution to journalArticlepeer-review

222 Citations (Scopus)


There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.

Original languageEnglish
Pages (from-to)587-597
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number3 PART 1
Publication statusPublished - 2011 Mar


  • Adaptive signal processing
  • brain computer interfaces
  • linear discriminant analysis
  • unsupervised learning

ASJC Scopus subject areas

  • Biomedical Engineering


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