English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28611/40652
Visitors : 767503      Online Users : 52
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46467

Title: Independent Component Ensemble of EEG for Brain–Computer Interface
Authors: Chun-Hsiang Chuang
Li-Wei Ko
Yuan-Pin Lin
Tzyy-Ping Jung
Chin-Teng Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Brain–computer interface (BCI);independent component analysis (ICA);multiple classifier system
Date: 2014-03
Issue Date: 2018-05-21T02:34:32Z
Publisher: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Abstract: Abstract:
Recently, successful applications of independent component analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasibility of using independent processes to elucidate human cognitive states. However, various technical problems arise in the building of an online brain-computer interface (BCI). These include the lack of an automatic procedure for selecting independent components of interest (ICi) and the potential risk of not obtaining a desired ICi. Therefore, this study proposes an ICi-ensemble method that uses multiple classifiers with ICA processing to improve upon existing algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of features of the resultant ICi; 3) the construction of parallel pipelines for effectively training multiple classifiers; and a 4) simple process that combines the multiple decisions. The proposed ICi-ensemble is demonstrated in a typical BCI application, which is the monitoring of participants' cognitive states in a realistic sustained-attention driving task. The results reveal that the proposed ICi-ensemble outperformed the previous method using a single ICi with ~ 7% (91.6% versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that characterizes the EEG dynamics of multiple brain areas favors the application of BCI in natural environments.
Relation: 22(2), pp.230-238
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46467
Appears in Collections:[資訊工程學系] 期刊論文

Files in This Item:

File Description SizeFormat
index.html0KbHTML59View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback