Development of a Steady-State Visually Evoked Potential (SSVEP)-Based Brain-Computer Interface for Typing Persian Texts

Document Type : Original Article (s)

Authors

1 MSc Student, Department of Bioelectric, School of Biomedical Engineering, Semnan University, Semnan, Iran

2 Assistant Professor, Department of Bioelectric, School of Biomedical Engineering, Semnan University, Semnan, Iran

Abstract

AbstractBackground: For disabled patients who are unable to use their muscles, brain-computer interface (BCI) systems can be used to establish a channel between their brain and outside world. Steady-state visually evoked potentials (SSVEP)-based interfaces are of brain-computer interface-spellers noted in recent years.Methods: In this study, stimulation patterns based on Braille code with eight flickering cues were used. MATLAB psychtoolbox was used for construction of the visual stimulation. Fast Fourier transform (FFT) method and maximum classifier were used for feature extraction and classification, respectively.Findings: We achieved 96.67% of classification accuracy and information transfer rate of 19.632 bit per minute using Steady-state visually evoked potentials brain response and Braille code.Conclusion: Because of advantages such as single electrode signal recording, low number of excitation frequencies and adjustable parameters such as rest time between the stimulations, designed system is highly efficient and user friendly.

Keywords

References

  1. Cao T, Wang X, Wang B, Wong CM, Wan F, Mak PU, et al. A high rate online SSVEP based brain-computer interface speller. Proceedings of the 5th International Conference IEEE/EMBS; 2011 Apr 27-May 1; Cancun, Mexico.
  2. Vilic A, Kjaer TW, Thomsen CE, Sorensen HB. DTU BCI speller: An SSVEP-based spelling system with dictionary support. Proceedings of the 35th Annual International Conference of the IEEE EMBS; 2013 Jul 3-7; Osaka, Japan.
  3. Won DO, Zhang HH, Guan C, Lee SW. A BCI speller based on SSVEP using high frequency stimuli design. Proceeding of the IEEE International Conference on Systems, Man and Cybernetics (SMC); 2014 Oct 5-8; San Diego, CA.
  4. Resalat SN, Setarehdan SK. An improved SSVEP based BCI system using frequency domain feature classification. Am J Biomed Eng 2013; 3(1): 1-8.
  5. Iscan Z, Dokur Z. A novel steady-state visually evoked potential-based brain-computer interface design: Character Plotter. Biomed Signal Process Control 2014; 10: 145-52.
  6. Brainard D, Ingling A, Kleiner M, Murray R, Pelli D, Broussard C. What's new in psychtoolbox-3. Perception 2007; 36(14): 1-16.
  7. Pires G, Nunes U, Castelo-Branco M. Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients. Clin Neurophysiol 2012; 123(6): 1168-81.
  8. Speier W, Deshpande A, Pouratian N. A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems. Clin Neurophysiol 2015; 126(6): 1171-7.
  9. Xu M, Qi H, Zhang L, Ming D. The parallel-BCI speller based on the P300 and SSVEP features. Proceedings of the 6th International Conference on IEEE EMBS Neural Engineering; 2013 Nov 6-8; Marina, CA.
  10. Hassanien AE, Azar AT. Brain-Computer Interfaces: Current Trends and Applications. Berlin, Germany: Springer; 2014.
Journal of Isfahan Medical School
Volume 34, Issue 393 - Serial Number 393
July and August 2016
Pages 914-918

Files

History

  • Receive Date: 18 July 2016
  • Revise Date: 26 May 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics