Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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High-rate BCI spelling System using eye-closed EEG signals

닫힌 눈(eye-closed) EEG신호를 이용한 높은 비율BCI 맞춤법 시스템

  • Nguyen, Trung-Hau (Department of Electronics Engineering, Pukyong National University) ;
  • Yang, Da-lin (Department of Electronics Engineering, Pukyong National University) ;
  • Kim, Jong-Jin (Department of Electronics Engineering, Pukyong National University) ;
  • Chung, Wan-Young (Department of Electronics Engineering, Pukyong National University)
  • Received : 2017.10.31
  • Accepted : 2017.12.20
  • Published : 2017.12.31
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Abstract

This study aims to develop an BCI speller utilizing eye-closed and double-blinking EEG based on asynchronous mechanism. The proposed system comprised a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected "eye-closed" event induces the "select" command, whereas a "double-blinking" (DB) event functions the "undo" command. A three-class support vector machine (SVM) classifier involving EEG signal analysis of three groups of events ("eye-open"-idle state, "eye-closed", and "double -blinking") is proposed. The results showed that the proposed BCI could achieve an overall accuracy of 92.6% and a spelling rate of 5 letters/min on average. Overall, this study showed an improvement of accuracy and the spelling rate resulting from in the feasibility and reliability of implementing a real-world BCI speller.

이 연구는 비동기 매커니즘을 바탕으로 닫힌 눈(eye-closed) 및 이중 블링크 (double-blinking) EEG를 사용하여 BCI를 개발하는 것을 목표한다. 제안된 시스템은 신호 처리 모듈과 그래픽 사용자 인터페이스 (VK-가상 키보드)로 구성되어 있으며 26개의 영문자와 특수 기호로 구성됩니다. "눈 닫기"이벤트는 "선택"(select)명령을 유발하는 반면, "이중 블링크"(DB) 이벤트는 "실행 취소"(undo) 명령에 따라 실행합니다. 3개의 이벤트 그룹 ("열린 눈"(eye-open, "닫힌 눈" (eye-closed)및 "이중 블링크"(double-blinking)에 대한 EEG 신호 분석과 관련된 3 등급 벡터 보조 분류 (SVM) 기계가 제안되었습니다. 결과는 제안된 BCI가 평균 92.6 %의 전체 정확도와 5 글자 / 분의 맞춤법 비율을 달성 할 수 있음을 보여주었습니다. 전반적으로 이 연구는 실제 BCI 맞춤법을 구현하기의 실현 가능성과 신뢰성으로 인해 정확도와 철자 비율의 향상을 보여주었습니다.

Keywords

References

  1. Birbaumer et al. "Breaking the silence: braincomputer interface (BCI) for communication and motor control," Psychophysiology, vol. 43(6), pp. 517-532, 2006. https://doi.org/10.1111/j.1469-8986.2006.00456.x
  2. Nicolas-Alonso et al. "Brain computer interfaces, a review," Sensor, vol. 12(2), pp. 1211-1279, 2012. https://doi.org/10.3390/s120201211
  3. Vaughan et al. "Brain-computer interface technology : a review of the Sensor International Meeting," IEEE Trans Neural Syst Rehabil Eng, vol. 11(2), pp. 94-109, 2003. https://doi.org/10.1109/TNSRE.2003.814799
  4. Allison et al. "ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system," IEEE Trans Neural Syst Rehabil Eng, vol. 11(2), pp. 110-113, 2003. https://doi.org/10.1109/TNSRE.2003.814448
  5. Kubler et al. "Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?," Clinical neurophysiology, vol. 119(11), pp. 2658-2666, 2008. https://doi.org/10.1016/j.clinph.2008.06.019
  6. Kindermans et al. "A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI," PloS one, vol. 7(4), pp. e3758, 2012.
  7. Throckmorton et al. "Bayesian approach to dynamically controlling data collection in P300 spellers," IEEE Trans Neural Syst Rehabil Eng, vol. 21(3), pp. 508-517, 2013. https://doi.org/10.1109/TNSRE.2013.2253125
  8. Blankertz et al. "The Berlin Brain-Computer Interface presents the novel mental typewriter Hex-o-Spell," In: Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course, 2006.
  9. Scherer et al. "An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate," IEEE Transactions on Biomedical Engineering, vol. 51(6), p. 979-984, 2004. https://doi.org/10.1109/TBME.2004.827062
  10. Cecotti et al. "A self-paced and calibration-less SSVEP-based brain-computer interface speller," IEEE Trans Neural Syst Rehabil Eng, vol. 18(2), pp. 127-133, 2010. https://doi.org/10.1109/TNSRE.2009.2039594
  11. Hwang et al. "Development of an SSVEP-based BCI spelling system adopting a QWERTYstyle LED keyboard," Journal of neuroscience methods, vol. 208(1), p. 59-65, 2012. https://doi.org/10.1016/j.jneumeth.2012.04.011
  12. Valbuena et al. "Spelling with the Bremen brain -computer interface and the integrated SSVEP stimulator," 2008.
  13. Volosyak et al. "SSVEP-based Bremen-BCI interface-boosting information transfer rates," Journal of neural engineering, vol. 8(3), p. 036020, 2011. https://doi.org/10.1088/1741-2560/8/3/036020
  14. Hazrati et al. "An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network," Medical engineering & physics, vol. 32(7), p. 730-739, 2010. https://doi.org/10.1016/j.medengphy.2010.04.016
  15. Hsu et al. "A Continuous EEG signal analysis for asynchronous BCI application," International journal of neural systems, vol. 21(4), p. 335-350, 2011. https://doi.org/10.1142/S0129065711002870