Wavelet-based Semblance for P300 Single-trial Detection

Carolina Saavedra, Laurent Bougrain

2013

Abstract

Electroencephalographic signals are usually contaminated by noise and artifacts making difficult to detect Event-Related Potential (ERP), specially in single trials. Wavelet denoising has been successfully applied to ERP detection, but usually works using channels information independently. This paper presents a new adaptive approach to denoise signals taking into account channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information in the wavelet domain to automatically select a temporal window which increases class separability. Results on a classic Brain-Computer Interface application to spell characters using P300 detection show that our algorithm has a better accuracy with respect to the VisuShrink wavelet technique and XDAWN algorithm among 22 healthy subjects, and a better regularity than XDAWN.

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Paper Citation


in Harvard Style

Saavedra C. and Bougrain L. (2013). Wavelet-based Semblance for P300 Single-trial Detection . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 120-125. DOI: 10.5220/0004191001200125

in Bibtex Style

@conference{biosignals13,
author={Carolina Saavedra and Laurent Bougrain},
title={Wavelet-based Semblance for P300 Single-trial Detection},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={120-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004191001200125},
isbn={978-989-8565-36-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Wavelet-based Semblance for P300 Single-trial Detection
SN - 978-989-8565-36-5
AU - Saavedra C.
AU - Bougrain L.
PY - 2013
SP - 120
EP - 125
DO - 10.5220/0004191001200125