title: An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials.
abstract: OBJECTIVE
  Current brain-computer interfaces (BCIs) are usually based on various, often supervised, signal processing methods. The disadvantage of supervised methods is the requirement to calibrate them with recently acquired subject-specific training data. Here, we present a novel algorithm for dimensionality reduction (spatial filter), that is ideally suited for single-trial detection of event-related potentials (ERPs) and can be adapted online to a new subject to minimize or avoid calibration time.
  
  
  METHODS
  The algorithm is based on the well-known xDAWN filter, but uses generalized eigendecomposition to allow an incremental training by recursive least squares (RLS) updates of the filter coefficients. We analyze the effectiveness of the spatial filter in different transfer scenarios and combinations with adaptive classifiers.
  
  
  RESULTS
  The results show that it can compensate changes due to switching between different users, and therefore allows to reuse training data that has been previously recorded from other subjects.
  
  
  CONCLUSIONS
  The presented approach allows to reduce or completely avoid a calibration phase and to instantly use the BCI system with only a minor decrease of performance.
  
  
  CONCLUSIONS
  The novel filter can adapt a precomputed spatial filter to a new subject and make a BCI system user independent.
authors: Hendrik
authors: H
authors: Woehrle
authors: Mario M
authors: MM
authors: Krell
authors: Sirko
authors: S
authors: Straube
authors: Su Kyoung
authors: SK
authors: Kim
authors: Elsa A
authors: EA
authors: Kirchner
authors: Frank
authors: F
authors: Kirchner
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D012815:Signal Processing, Computer-Assisted