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Related Experiment Video

Updated: Jun 21, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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Selecting features for BCI control based on a covert spatial attention paradigm.

Marcel van Gerven1, Ali Bahramisharif, Tom Heskes

  • 1Institute for Computing and Information Sciences, Intelligent Systems Group, The Netherlands. marcelge@cs.ru.nl

Neural Networks : the Official Journal of the International Neural Network Society
|July 7, 2009
PubMed
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This study enhances brain-computer interfaces using Magnetoencephalography (MEG) and sparse logistic regression for covert spatial attention tasks. Improved classification accuracy was achieved by selecting subject-specific sensors and optimizing trial numbers.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Covert spatial attention is a novel control signal for brain-computer interfaces (BCIs).
  • Previous electroencephalography (EEG) studies utilized alpha power for classification in spatial attention paradigms.
  • Magnetoencephalography (MEG) offers higher spatial resolution compared to EEG.

Purpose of the Study:

  • To reexamine the covert spatial attention paradigm using MEG.
  • To improve classification performance in MEG-based BCIs.
  • To investigate the impact of attention period length and trial number on classification accuracy.

Main Methods:

  • Utilized MEG recordings from fifteen subjects performing a covert spatial attention task.
  • Applied sparse logistic regression to select subject-specific sensor subsets for classification.

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Last Updated: Jun 21, 2026

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  • Analyzed classification performance as a function of attention period duration and number of trials.

    • Main Results:

    • Sparse logistic regression improved classification performance by selecting optimal sensor subsets.
    • Classification accuracy increased with longer attention periods, though bit rates did not always improve.
    • Peak classification performance was achieved with approximately 150 trials, requiring ~11 minutes of training.

    Conclusions:

    • MEG combined with sparse logistic regression offers enhanced BCI control via covert spatial attention.
    • Subject-specific sensor selection is crucial for optimizing BCI performance.
    • Understanding the relationship between attention duration, trial number, and classification accuracy is key for efficient BCI design.