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Sequenced subjective accents for brain-computer interfaces.

R J Vlek1, R S Schaefer, C C A M Gielen

  • 1Donders Institute for Brain, Cognition and Behaviour: Centre for Cognition, Radboud University, Montessorilaan 3, 6525 HE, Nijmegen, The Netherlands. r.vlek@donders.ru.nl

Journal of Neural Engineering
|April 6, 2011
PubMed
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Subjective accenting, a voluntary cognitive process, shows promise for brain-computer interfaces (BCI). Researchers decoded imagined accent patterns from EEG data, achieving promising bit rates for auditory BCI applications.

Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Subjective accenting is a cognitive process where regular auditory pulses are perceived as having an accent pattern.
  • This process is voluntarily controlled, making it a potential communication channel for brain-computer interface (BCI) systems.

Purpose of the Study:

  • To investigate the feasibility of subjective accenting as a BCI paradigm.
  • To optimize decoding of subjective accenting patterns from non-invasive electroencephalography (EEG) data.

Main Methods:

  • Ten subjects perceived and imagined different metric patterns (2-, 3-, and 4-beat) with a metronome.
  • Offline classification was used to distinguish imagined accented from non-accented beats on a single-trial basis (0.5 s).
  • Decoding accuracy was assessed using a sequence classification algorithm, and performance was measured by bit rate.

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Main Results:

  • Average classification accuracy for imagined accents reached 60.4% across subjects on a single-trial level.
  • Decoding of imagined accents was successful even with a classifier trained on perception data.
  • The best-case scenario translated to an average bit rate of 4.4 bits per minute, indicating successful decoding of cyclic accent patterns.

Conclusions:

  • Subjective accenting is a feasible paradigm for BCI applications.
  • The time-structured nature of subjective accenting can be effectively exploited for decoding from EEG data.
  • This paradigm shows potential for developing online auditory BCIs.