Stationary subspace analysis

Paul Von Bunau, Frank C. Meinecke, Klaus Robert Muller

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


Non-stationarities are an ubiquitous phenomenon in time- series data, yet they pose a challenge to standard methodology: classification models and ICA components, for example, cannot be estimated reliably under distribution changes because the classic assumption of a stationary data generating process is violated. Conversely, understanding the nature of observed non-stationary behaviour often lies at the heart of a scientific question. To this end, we propose a novel unsupervised technique: Stationary Subspace Analysis (SSA). SSA decomposes a multi- variate time-series into a stationary and a non-stationary subspace. This factorization is a universal tool for furthering the understanding of non- stationary data. Moreover, we can robustify other methods by restricting them to the stationary subspace. We demonstrate the performance of our novel concept in simulations and present a real world application from Brain Computer Interfacing.


  • BCI
  • BSS
  • Brain-computer-interface
  • Covariate shift
  • Dimensionality reduction
  • Non-stationarities
  • Source separation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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