Outlier Detection for Single-Trial EEG Signal Analysis

被引：0

作者：

Wang, Boyu ^{[1
]}

Wan, Feng ^{[1
]}

Mak, Peng Un ^{[1
]}

Mak, Pui In ^{[1
]}

Vai, Mang I. ^{[1
]}

机构：

[1] Univ Macau, Fac Sci & Technol, Dept Elect & Elect Engn, Taipa, Macau, Peoples R China

来源：

2011 5TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) | 2011年

关键词：

BRAIN; CLASSIFICATION; ALGORITHMS;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The performance of a brain computer interface (BCI) system is usually degraded due to the outliers in electroencephalography (EEG) samples. This paper presents a novel outlier detection method based on robust learning of Gaussian mixture models (GMMs). We apply the proposed method to the single-trial EEG classification task. After trial-pruning, feature extraction and classification are performed on the subset of training data, and experimental results demonstrate that the proposed method can successfully detect the outliers and therefore achieve more reliable result.

引用

页码：478 / 481

页数：4

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