닫힌 눈 (eye-closed) EEG신호를 이용한 높은 비율BCI 맞춤법 시스템
High-rate BCI spelling System using eye-closed EEG signals
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초록
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)명 령을 유발하는 반면, “이중 블링크”(DB) 이벤트는 “실행 취소”(undo) 명령에 따라 실행합니다. 3개의 이벤트 그 룹 (“열린 눈”(eye-open, “닫힌 눈” (eye-closed)및 “이중 블링크”(double-blinking)에 대한 EEG 신호 분석과 관 련된 3 등급 벡터 보조 분류 (SVM) 기계가 제안되었습니다. 결과는 제안된 BCI가 평균 92.6 %의 전체 정확도 와 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
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 spelle
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