P300 detection using nonlinear independent component analysis

Author

Turnip, Arjon ; Siahaan, Mery ; Suprijanto ; Waafi, Affan Kaysa

Author_Institution

Tech. Implementation Unit for Instrum. Dev., Indonesian Inst. of Sci., Bandung, Indonesia

fYear

2013

fDate

28-30 Aug. 2013

Firstpage

104

Lastpage

109

Abstract

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.

Keywords

bioelectric potentials; brain-computer interfaces; electroencephalography; independent component analysis; medical signal detection; psychology; signal classification; AAR algorithm; JADE algorithm; NICA extraction method; SOBI algorithm; brain signal based EEG-P300 detection; continuous mental state classification; nonlinear independent component analysis extraction method; Accuracy; Classification algorithms; Electrodes; Electroencephalography; Feature extraction; Signal to noise ratio; Vectors; Brain computer interface (BCI); Classification accuracy; ICA Electroencephalogram (EEG); Nonlinear; Transfer rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation Control and Automation (ICA), 2013 3rd International Conference on

Conference_Location

Ungasan

Print_ISBN

978-1-4673-5795-1

Type

conf

DOI

10.1109/ICA.2013.6734054

Filename

6734054