title: Ensembles of adaptive spatial filters increase BCI performance: an online evaluation.
abstract: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem.
  
  
  
  Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches.
  
  
  
  The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency.
  
  
  
  CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Marchstr. 23, 10587 Berlin, Germany.
authors.forename: Claudia
authors.initials: C
authors.lastname: Sannelli
authors.forename: Carmen
authors.initials: C
authors.lastname: Vidaurre
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
mesh_terms: D000465:Algorithms; D058256:Brain Waves; D062207:Brain-Computer Interfaces; D002138:Calibration; D004569:Electroencephalography / Q000706:statistics & numerical data; D006801:Humans; D007092:Imagination / Q000502:physiology; D000069550:Machine Learning; D009068:Movement / Q000502:physiology