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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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On Visually Evoked Potentials in EEG Induced by Multiple Pseudorandom Binary Sequences for Brain Computer Interface

H Nezamfar1, U Orhan, D Erdogmus

  • 1Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|September 7, 2013
PubMed
Summary
This summary is machine-generated.

This study explored using multiple m-sequences for brain-computer interfaces. Higher bit presentation rates showed mixed results on classification accuracy and speed, requiring further investigation.

Keywords:
Brain computer interfaceEEGSSVEPelectroencephalographypseudorandom M-sequencevisually evoked potential

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Visually evoked potentials are crucial for brain-computer interface (BCI) design.
  • P300 responses and steady-state visual evoked potentials are common BCI signals.
  • M-sequences offer non-periodic visual stimuli for BCI research.

Purpose of the Study:

  • To investigate the use of multiple m-sequences for intent discrimination in BCIs.
  • To determine the impact of m-sequence bit presentation rate on classification accuracy and speed.

Main Methods:

  • Utilized four distinct m-sequences of length 31.
  • Acquired electroencephalography (EEG) data at 15Hz and 30Hz bit presentation rates.
  • Compared two basic classifier schemes for intent discrimination.

Main Results:

  • Mixed results were observed across subjects regarding the effect of bit presentation rate.
  • One subject showed potential for increased bit rates without accuracy loss, enabling faster decisions.
  • Another subject's data did not support this conclusion.

Conclusions:

  • The impact of m-sequence bit presentation rate on BCI performance requires further study.
  • Advanced signal processing, particularly EEG channel information fusion, is needed.
  • Optimizing m-sequence parameters could enhance BCI speed and accuracy.