Proceedings of the KOSOMBE Conference (대한의용생체공학회:학술대회논문집)
- Volume 1995 Issue 05
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- Pages.160-163
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- 1995
Development of an Optimal EEG and Artifact Classifier Using Neural Network Operating Characteristics
신경망 운영특성곡선을 이용한 최적의 뇌파 및 Artifact 분류기 구성
Abstract
An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.
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