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  • 1Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany.

Brain Sciences
|February 25, 2022
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This study shows that reusing training data from different sessions significantly improves brain-computer interface (BCI) accuracy for code-modulated visual evoked potentials (cVEP). Even two blocks from separate sessions boosted performance, with five blocks exceeding 90% accuracy.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Code-modulated visual evoked potentials (cVEP)-based brain-computer interfaces (BCIs) are popular due to their autocorrelation properties.
  • cVEP classification typically relies on subject-specific templates derived from pre-recorded EEG responses.
  • System accuracy is directly influenced by the volume of collected user training data.

Purpose of the Study:

  • To investigate the impact of repetitive block-wise training on cVEP-based BCI classification accuracy.
  • To evaluate the effectiveness of reusing previously recorded training data across different sessions.
  • To determine optimal training data configurations for enhanced BCI performance.

Main Methods:

  • An offline study using previously recorded EEG data from 10 participants across multiple sessions.
Keywords:
BCI spellerbrain–computer interface (BCI)canonical correlation analysis (CCA)code-modulated visual evoked potentials (cVEP)task-related component analysis (TRCA)

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  • Template matching target identification utilizing models similar to task-related component analysis (TRCA).
  • Spatial filter generation via canonical correlation analysis (CCA).
  • Interchangeable comparison of training data blocks from different sessions to find reliable configurations.

    • Main Results:

    • Intra-session accuracy reached 94.84%, while inter-session accuracy was 76.67%.
    • Models using only two training blocks from different sessions achieved an average accuracy of 82.66%.
    • An average accuracy exceeding 90% required at least five training blocks.

    Conclusions:

    • Reusing previously recorded training data can significantly enhance cVEP-based BCI performance.
    • The block-wise training approach, especially with data from multiple sessions, offers a viable method for improving classification accuracy.
    • This strategy provides a pathway to more efficient and accurate BCI systems by leveraging existing data.