Self-Labeling for P300 Detection
- 주제(키워드) Brain-computer interface (BCI) , P300 component
- 발행기관 포항공과대학교 일반대학원
- 지도교수 최승진
- 발행년도 2012
- 학위수여년월 2012. 2
- 학위명 석사
- 학과 및 전공 일반대학원 정보전자융합공학부
- 실제URI http://www.dcollection.net/handler/postech/000001216509
- 본문언어 한국어
- 저작권 포항공과대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
A brain-computer interface (BCI) is a communication system that uses brain activities to control computers. A P300-based BCI is an interface that uses one of the visual-evoked potentials which is an event related potential that has been elicited by task-relevant stimuli. Before using BCI, the calibration time is very important for the system to obtain high performance, where a long calibration time blocks the ability of BCIs to be widely used. In this thesis, we present an algorithm for reducing the calibration time for the P300-based BCI. Our proposed algorithm uses a small set of training data in the calibration time without sacrificing detecting performance. This algorithm uses singular value decomposition and linear discriminant analysis with minimum distance classifier. Furthermore, in order to compensate the information loss due to a small set of training data, this algorithm selectively accumulates the new signals to training data during test procedure, which is mentioned as ‘Self-labeling’. To verify our algorithm, we carried out two experiments and proved that our algorithm outperforms an ordinary algorithm with a small set of training data.
more목차
1 Introduction = 1
1.1 Overview of Brain-Computer Interface = 1
1.2 Motivation = 2
1.3 Outline = 3
2 Method = 4
2.1 Data Acquisition = 4
2.2 Preprocessing = 5
2.3 Feature Extraction: Singular Value Decomposition = 6
2.4 P300 Dectection: LDA and Minimum Distance Classifier = 8
2.5 Self-Labeling = 9
3 Experiment = 13
3.1 Training with small data set = 13
3.2 Training with other subject’s training data = 18
4 Conclusion = 21
한글 요약문 = 23
Bibliography = 24