Classification of EEG-P300 Signals Using Fisher's Linear Discriminant Analysis

被引：0
|
作者：
Turnip, Arjon [1 ]
Widyotriatmo, Augie [2 ]
Suprijanto [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; AAR; JADE; SOBI; EEG;
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 classifier using Fisher's Linear Discriminant Analysis is used to investigate the performance of three different extraction methods for brain signal based electroencephalogram (EEG)-P300. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is presented. 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：98 / 103
页数：6
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