Difference between revisions of "A genetic algorithm for automatic feature extraction from EEG data"

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(Project short description)
m (Part 1: project profile)
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*[[User:RiccardoCattaneo | Riccardo Cattaneo]]
*[[User:RiccardoCattaneo | Riccardo Cattaneo]]
*[[User:GiuseppeChindemi | Giuseppe Chindemi]]
*[[User:GiuseppeChindemi | Giuseppe Chindemi]]
===== Students who worked on the project in the past  =====
*[[User: LuigiDallavalle | Luigi Dallavalle]]
*[[User: FlavioMutti | Flavio Mutti]]
== '''Part 2: project description''' ==
== '''Part 2: project description''' ==

Revision as of 03:52, 12 March 2009

Part 1: project profile

Project name

A genetic algorithm for automatic feature extraction from EEG data

Project short description

The identification of sight-related stimuli (typically some sort of flash) based upon EEG-analysis needs a specific software capable to find and extract a set of relevant stimuli-related features from those EEG. In particular we are looking for a stimulus called P300 (a positive deflection found, in the EEG, after more or less 300ms from the stimulation). The application we are further developing aims at finding those features that best describe everyone's partcular P300. Such an application, based upon an ad-hoc genetic algorithm, has already been developed at Politecnico with excellent results. The goal of this project is producing a c++ version of this software in order to maximize efficiency and portability.


  • Start date: 2008/10/01
  • End date: 2009/04/30

People involved

Project head(s)
Students currently working on the project
Students who worked on the project in the past

Part 2: project description

State of the art

It is currently under development the online component of the identification section of the code and the class to which is delegated the computation of the P300 template.

Preliminary studies and sketches

After a deep analysis of the pre-existing source code, a c++ flavor of an analogous matlab version, we moved towards the current phase of further development and conversion of the application.

Design notes and guidelines

Our attention is focused on portability and efficiency; due to this fact we are currently developing the software in the form of a c++ class.

Experiments (description and results)

After we will have developed the application in its entireness, it's been planned to carry on some experiments related to fitness, genes and parameters tweaking.


Savane - BioSignal Analysis Toolkit (you must be a registered user to log in the repository of Savane)

Part 3: documents and references


A Genetic Algorithm for Automatic Feature Extraction in P300 Detection

A Genetic Algorithm Tutorial