Subject-independent mental state classification in single trials

Siamac Fazli, Florin Popescu, Márton Danóczy, Benjamin Blankertz, Klaus Robert Müller, Cristian Grozea

Research output: Contribution to journalArticlepeer-review

158 Citations (Scopus)

Abstract

Current state-of-the-art in Brain Computer Interfacing (BCI) involves tuning classifiers to subject-specific training data acquired from calibration sessions prior to functional BCI use. Using a large database of EEG recordings from 45 subjects, who took part in movement imagination task experiments, we construct an ensemble of classifiers derived from subject-specific temporal and spatial filters. The ensemble is then sparsified using quadratic regression with ℓ1 regularization such that the final classifier generalizes reliably to data of subjects not included in the ensemble. Our offline results indicate that BCI-naïve users could start real-time BCI use without any prior calibration at only very limited loss of performance.

Original languageEnglish
Pages (from-to)1305-1312
Number of pages8
JournalNeural Networks
Volume22
Issue number9
DOIs
Publication statusPublished - 2009 Nov

Keywords

  • BCI
  • Machine learning
  • Zero-training

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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