NDLI: Self-labeling for P300 detection

Content Provider	IEEE Xplore Digital Library
Author	Sangmin Lee Yunjun Nam Seungjin Choi
Copyright Year	2012
Description	Author affiliation: Division of IT Convergence Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, 790-784, Korea (Sangmin Lee; Seungjin Choi) \|\| School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, 790-784, Korea (Yunjun Nam)
Abstract	The P300 wave refers to a positive peak with a latency of 300 ms, produced in response to task-relevant stimuli. It is a widely-used event related potential (ERP) in practical brain computer interface (BCI) systems, where a classifier is trained using discriminative features extracted from a set of labeled examples, in order to detect the presence of P300. Given a small training examples, the intra- and inter-subject variations in amplitude and latency of P300 degrade the performance of classifier. Thus, it requires a longer training period (calibration time) with more labeled examples for satisfactory performance. In this paper we present a self-labeling method, where confident unlabeled data, together with their predicted labels, are gradually added to the training set, in order to re-train the classifier. Linear discriminant analysis with singular value decomposition is used to progressively extract discriminate features. Experiments demonstrate the high performance of our method, especially in the case where a small number of training examples are available. We also apply the method to the zero-calibration P300-based BCI, which removes subject-dependent calibration procedures by using the training set already recorded from other subjects.
Starting Page	268
Ending Page	273
File Size	330833
Page Count	6
File Format	PDF
ISBN	9781467317139
e-ISBN	9781467317146
e-ISBN	9781467317122
DOI	10.1109/ICSMC.2012.6377712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training data Training Accuracy Feature extraction Vectors Calibration Electroencephalography singular value decomposition (SVD) Brain computer interface (BCI) linear discriminant analysis (LDA) P300 detection self-labeling
Content Type	Text
Resource Type	Article

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