한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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뇌-컴퓨터 인터페이스를 위한 개인의 특성을 반영하는 뇌파 분류기

An EEG Classifier Representing Subject's Characteristics for Brain-Computer Interface

  • 발행 : 2000.01.15
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⟨ 이전 논문 다음 논문 ⟩

초록

인간의 생각만으로 기계를 작동할 수 있게 하는 인터페이스 시스템에 관한 연구 분야인 BCI (Brain-Computer Interface)에서는 피험자의 두피로부터 EEG(Electroencephalograph)를 측정하고 인식하여 뇌 상태를 알아내고 그 결과를 기계의 조종에 응용하는 방법을 사용한다. 본 연구에서는 각 개인으로부터 고유의 뇌파인 EEG를 얻고 신호처리하여 인식하는 인식모델을 제안하였다. 제안된 모델은 특정 작업을 수행하고 있을 때의 EEG 신호로부터 인식에 중요한 영향을 미치는 특징들을 추출해 내고, 이를 인식에 이용한다. 제안된 모델은 인식할 EEG 패턴들을 두개씩 분류하여 각각을 인식한 후, 그 결과를 종합하여 최종적인 인식결과를 얻도록 하였다. 본 연구의 실험에서는 피험자가 4가지의 작업을 수행하는 동안 얻어지는 4가지 EEG 패턴을 인식하였다. 제안된 모델은 90%이상의 높은 인식율을 보였고, 각 피험자에게 독특하게 존재하는 특징들을 인식 결과로서 제공하였다. 제안된 모델의 높은 인식율과 빠른 처리속도는 실시간 BCI 시스템에 응용될 수 있는 가능성을 보여주고 있다.

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

키워드

참고문헌

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