A Bayesian approach for spatio-spectral filter optimization in BCI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel Bayesian frame-work for discriminative feature extraction for motor imagery classification in an EEG-based BCI, in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatio-spectral filter optimization is formulated as the estimation of an unknown posterior pdf that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on two public databases.

Original languageEnglish
Title of host publication2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages22-23
Number of pages2
DOIs
Publication statusPublished - 2013 May 17
Event2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of
Duration: 2013 Feb 182013 Feb 20

Other

Other2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
CountryKorea, Republic of
CityGangwon Province
Period13/2/1813/2/20

Keywords

  • Bayesian Frame-work
  • Brain-Computer Interface
  • Motor Imagery Classification
  • Spatio-Spectral Filter Optimization

ASJC Scopus subject areas

  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'A Bayesian approach for spatio-spectral filter optimization in BCI'. Together they form a unique fingerprint.

  • Cite this

    Suk, H-I., & Lee, S. W. (2013). A Bayesian approach for spatio-spectral filter optimization in BCI. In 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 (pp. 22-23). [6506616] https://doi.org/10.1109/IWW-BCI.2013.6506616