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논문 기본 정보

자료유형
학술저널
저자정보
Yongseok Yoo (Incheon National University)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.17 No.2
발행연도
2017.6
수록면
68 - 75 (8page)
DOI
10.5391/IJFIS.2017.17.2.68

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초록· 키워드

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The P300 wave in electroencephalography (EEG) has been widely used for brain–computer interfaces (BCIs). The purpose of this study is to reduce the large latency in detecting P300 wave using sequential detection theory. The probability of a P300 wave from each EEG measurement is calculated using an ensemble of support vector machines with simple postprocessing. The sequential detection of a P300 wave is formulated as either binary or multiple hypothesis tests. A decision is made as soon as the accumulated probability of a P300 wave reaches decision boundaries given by Wald’s approximation. The experimental results agreed with the theory and often showed fewer errors than predicted. With binary hypotheses, the probabilities of a miss and a false positive were close to and often lower than theoretical predictions. For multiple hypothesis tests, sequential detection required much fewer samples than fixed-sample-size detection to achieve the same accuracy. Thus, sequential detection of P300 waves enables a high accuracy and low latency of BCIs.

목차

Abstract
1. Introduction
2. Calculation of P300 Probabilities from Individual Trials
3. Sequential Detection of P300 Waves
4. Results
5. Discussion
References

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