Improvement of Classification Accuracy in a Phase-Tagged Steady-State Visual Evoked Potential-Based Brain–Computer Interface Using Adaptive Neuron-Fuzzy Classifier

Hao Teng Hsu, Po Lei Lee, Kuo Kai Shyu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Steady-state visual evoked potential (SSVEP) has been used to design brain–computer interface (BCI) for a variety of applications, due to its advantages of high accuracy, fewer electrodes, and high information transfer rate. In recent years, researchers developed phase-tagged SSVEP-based BCI to overcome the problem of amplitude–frequency preference in traditional frequency-coded SSVEPs. However, the phase of SSVEP could be affected by subject’s attention and emotion, which sometimes causes ambiguity in discerning gazed targets when fixed phase margins were used for class classification. In this study, we adopted adaptive neuron-fuzzy classifier (ANFC) to improve the gaze-target detections. The SSVEP features in polar coordinates were first transformed into Cartesian coordinates, and then ANFC was utilized to improve the accuracy of gazed-target detections. The proposed ANFC-based approach has achieved 63.07 ± 8.13 bits/min.

Original languageEnglish
Pages (from-to)542-552
Number of pages11
JournalInternational Journal of Fuzzy Systems
Volume19
Issue number2
DOIs
StatePublished - 1 Apr 2017

Keywords

  • Adaptive neuron-fuzzy classifier
  • BCI
  • SSVEP

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