Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Pattern classification of the synchronized EEG records by an auditory stimulus for human-computer interface

인간-컴퓨터 인터페이스를 위한 청각 동기방식 뇌파신호의 패턴 분류

  • 이용희 (한라대학교 공과대학 컴퓨터공학과) ;
  • 최천호 (한라대학교 정보산업대학원)
  • Published : 2008.12.30
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Abstract

In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.

본 논문에서는 정상인의 정신적인 뇌 활동에 의한 순수한 뇌파를 측정하고 효과적으로 분류하기 위한 방법을 제시한다. 과정은 대상자가 특정한 작업에 대해 생각하게 하고 이때의 뇌파를 청각이벤트에 동기시켜 측정하고, 측정된 뇌파의 기준선의 이동과, 생리적인 아티펙트의 영향을 줄인다. 마지막으로, 평균가산법에 의해 정신적인 작업에 대한 신호만을 추출하고 AR 계수를 가지고 인식작업을 수행한다. 실험에서, 청각자극을 이벤트로 사용하였으며, 뇌파의 도출은 $C_3-A_1$, $C_4-A_2$, $P_Z-A_1$의 3채널에서 기록하였다. 각 채널당 16회의 평균가산후에, 12차 AR계수로 특정한 정신적인 작업에 대한 특징을 추출하였다. 전체 36개의 특징계수를 신경망의 입력으로 사용하고, 각 작업 당 50회를 훈련데이터로 사용하였다. 제안한 방법의 인식률은 2종류 작업에 대해 34-92%, 4종류 작업에 대해 38-54%를 얻었다.

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