Single Trial P300 detection based on the Empirical Mode Decomposition

Author

Solis-Escalante, Teodoro ; Gentiletti, Gerardo Gabriel ; Yanez-Suarez, Oscar

Author_Institution

Dept. of Electr. Eng., Univ. Autonoma Metropolitana, Mexico City

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

1157

Lastpage

1160

Abstract

We present a new method for single trial detection of P300 evoked responses. The features used to classify are the coefficients of a least-squares fit of a single EEG epoch to the intrinsical mode functions of an empirical mode decomposition of the averaged event response from a P300 training set. Support vector machines with a linear kernel are used to classify the epochs and receiver operating characteristic analysis is used to evaluate our method´s performance

Keywords

bioelectric potentials; electroencephalography; feature extraction; learning (artificial intelligence); least squares approximations; matrix decomposition; medical signal processing; sensitivity analysis; signal classification; support vector machines; P300 evoked responses; SVM classification; empirical mode decomposition; feature extraction; intrinsical mode functions; least-squares fit; linear kernel; receiver operating characteristic analysis; single trial EEG detection; support vector machines; Cities and towns; Electrodes; Electroencephalography; Enterprise resource planning; Feature extraction; Signal to noise ratio; Support vector machine classification; Support vector machines; Testing; USA Councils;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.260589

Filename

4461962