Feature extraction with multiscale autoregression of multichannel time series for P300 speller BCI

Author

He, Lin ; Gu, Zhenghui ; Li, Yuanqing ; Yu, Zhuliang

Author_Institution

Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China

fYear

2010

fDate

14-19 March 2010

Firstpage

610

Lastpage

613

Abstract

P300 is one of the most studied components of event related potentials which reflects the responses of brain to events in the external environment. In this paper, we present a new method that utilizes multiresolution autoregression of multichannel time series (MAMTS) for feature extraction of P300 wave. First, it adopts multiresolution autoregression on dyadic tree to depict the characteristic of electroencephalogram (EEG) signal. Then the corresponding autoregression noise of multichannel time series is extracted as the feature. The experiment results verified the effectiveness of this new feature for P300 speller brain compute interface (BCI).

Keywords

autoregressive processes; brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; neurophysiology; time series; EEG; P300 speller BCI; autoregression noise; brain; brain compute interface; dyadic tree; electroencephalogram signal; event related potentials; external environment; feature extraction; multichannel time series; multiresolution autoregression; Automation; Brain computer interfaces; Educational institutions; Electroencephalography; Feature extraction; Helium; Keyboards; Signal analysis; Signal resolution; Time series analysis; BCI; P300 speller; feature extraction; multichannel time series; multiresolution autoregression;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495194

Filename

5495194