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논문 기본 정보

자료유형
학술저널
저자정보
XinYang Yu (Chung-Ang University) Seung-Min Park (Chung-Ang University) Kwang-Eun Ko (Chung-Ang University) Kwee-Bo Sim (Chung-Ang University)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.13 No.1
발행연도
2013.3
수록면
11 - 18 (8page)

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초록· 키워드

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Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of-the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with μ and β bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

목차

Abstract
1. Introduction
2. Related Works
3. Discriminant Power Feature Selection using PCA and Classification using Support Vector Machine
4. BCI Experiment for Motor Imagery EEG Classification
5. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2014-020-002591654