A zero-training algorithm for EEG single-trial classification applied to a face recognition ERP experiment

Author

Lage-Castellanos, Agustín ; Nieto, Juan I. ; Quiñones, Ileana ; Martínez-Montes, Eduardo

Author_Institution

Australian Centre for field Robot., Univ. of Sydney, Sydney, NSW, Australia

fYear

2010

fDate

Aug. 31 2010-Sept. 4 2010

Firstpage

4209

Lastpage

4212

Abstract

This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface (BCI) systems are areas with a significant amount of potential.

Keywords

brain-computer interfaces; electroencephalography; face recognition; learning (artificial intelligence); medical signal processing; BCI; EEG; ERP; brain computer interface; face recognition; single-trial classification; subject-specific training data; zero-training algorithm; Brain computer interfaces; Databases; Electrodes; Electroencephalography; Face; Feature extraction; Training; Algorithms; Electroencephalography; Evoked Potentials; Face; Humans; Visual Perception;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Conference_Location

Buenos Aires

ISSN

1557-170X

Print_ISBN

978-1-4244-4123-5

Type

conf

DOI

10.1109/IEMBS.2010.5627395

Filename

5627395