P300 Detection Using Nonlinear Independent Component Analysis

被引：0
|
作者：
Turnip, Arjon [1 ]
Siahaan, Mery [1 ]
Suprijanto [2 ]
Waafi, Affan Kaysa [2 ]
机构：
[1] Indonesian Inst Sci, Tech Implementat Unit Instrumentat Dev, Bandung, Indonesia
[2] Bandung Inst Technol ITB, Fac Ind Technol, Instrumentat & Control Res Grp, Bandung, Indonesia
来源：
2013 3RD INTERNATIONAL CONFERENCE ON INSTRUMENTATION CONTROL AND AUTOMATION (ICA 2013) | 2013年
关键词：
Brain computer interface (BCI); Classification accuracy; Transfer rate; Nonlinear; ICA Electroencephalogram (EEG); BRAIN-COMPUTER INTERFACE; SEPARATION; BCI;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well-known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.
引用
收藏
页码：104 / 109
页数：6
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