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Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface


Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as "yes" or "no" or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct "yes"/"no" BCI from a single session. Blind source separation (BSS) and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA) wrapped around a support vector machine (SVM) classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a "direct," single-session BCI.

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Correspondence to David A. Peterson.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Peterson, D.A., Knight, J.N., Kirby, M.J. et al. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface. EURASIP J. Adv. Signal Process. 2005, 218613 (2005).

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Keywords and phrases:

  • electroencephalogram
  • brain-computer interface
  • feature selection
  • independent components analysis
  • support vector machine
  • genetic algorithm