By Topic

Recognition and classification of P300s in EEG signals by means of feature extraction using wavelet decomposition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
S. Costagliola ; Politecnico di Milano, Dipartimento di Elettronica e Informazione, IIT unit, Via Ponzio 34/5, 20133, Italy ; B. Dal Seno ; M. Matteucci

In the last twenty years the understanding of the brain function and the advent of powerful low-cost computer equipment allowed the birth and the development of the BCI (brain-computer interface), a device that interprets brain activity to issue commands. P300 is a positive peak at about 300 ms from a stimulus, and has been used as a base for a BCI in many studies. The aim of this research consists in recognizing and classifying P300 signals by using wavelet transforms. This study analyzes both the kind of wavelets and which coefficients are more suited for a 100% correct decisions using as few repetitions of stimuli as possible. The classifier performs a quadratic discriminant analysis. The method is tested on the ldquoBCI Competition 2003rdquo data set IIb with excellent results.

Published in:

2009 International Joint Conference on Neural Networks

Date of Conference:

14-19 June 2009