Title :
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
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;
Conference_Titel :
Instrumentation Control and Automation (ICA), 2013 3rd International Conference on
Conference_Location :
Ungasan
Print_ISBN :
978-1-4673-5795-1
DOI :
10.1109/ICA.2013.6734054
