Difference between revisions of "BioSignal Analysis"

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[[Category:Research Area]]
 
[[Category:Research Area]]
We develop systems to analyze biological signals to understand high-level features of people producing them.
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We develop systems to analyze biological signals to understand high-level features of people producing them. This area shares projects with the [[Affective Computing]] area.
  
 
==Ongoing Projects==
 
==Ongoing Projects==

Revision as of 15:08, 2 April 2010

We develop systems to analyze biological signals to understand high-level features of people producing them. This area shares projects with the Affective Computing area.

Ongoing Projects

Affective Computing And BioSignals

Brain-Computer Interface

People


Project Proposals

Wiki Page: Aperiodic visual stimulation in a VEP-based BCI
Bci arch.png

Title: Aperiodic visual stimulation in a VEP-based BCI
Description: Visual-evoked potentials (VEPs) are a possible way to drive the a Brain-Computer Interface (BCI). This projects aims at maximizing the discrimination between different stimuli by using numerical codes derived from techniques of digital telecommunications.

Tutor: MatteoMatteucci
Additional Info: CFU 5 / Bachelor of Science / Course, Thesis

Wiki Page: Creation of new EEG training by introduction of noise
Bci arch.png

Title: Creation of new EEG training by introduction of noise
Description: A Brain-Computer Interface (BCI) must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

Tutor: MatteoMatteucci
Additional Info: CFU 5 - 20 / Bachelor of Science, Master of Science / Course, Thesis

Wiki Page: Driving an autonomous wheelchair with a P300-based BCI
LURCH wheelchair.jpg

Title: Driving an autonomous wheelchair with a P300-based BCI
Description: This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair (LURCH) with a BCI, through the development of key software modules. Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.
Tutor: MatteoMatteucci
Additional Info: CFU 5 - 20 / Bachelor of Science, Master of Science / Course

Wiki Page: Exploratory data analysis by genetic feature extraction
Evolve1at300dpi.gif

Title: Exploratory data analysis by genetic feature extraction
Description: Understanding the waves in EEG signals is an hard task and psicologists often need automatic tools to perform this task. In this project we are interested in using a genetic algorithm developed for P300 feature extraction in order to extract useful informations from Error Potentials. The project is a collaboration with the psicology department od Padua University.

Tutor: MatteoMatteucci
Additional Info: CFU 5 - 20 / Master of Science / Course, Thesis

Wiki Page: Multimodal GUI for driving an autonomous wheelchair
LURCH wheelchair.jpg

Title: Multimodal GUI for driving an autonomous wheelchair
Description: This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair (LURCH - The autonomous wheelchair) with a multi modal interface (Speech Recognition, Brain-Computer Interface, etc.), through the development of key software modules. The work will be validated with live experiments.

Tutor: MatteoMatteucci, SimoneCeriani, DavideMigliore
Additional Info: CFU 5 - 10 / Bachelor of Science, Master of Science / Course

Wiki Page: P300 BCI

Title: P300 BCI for ALS patient
Description: Recovery, integration and adaptation of P300 BCI (hardware and software) stubs to generate a working interface for a speller. The aim is to develop a working prototype for an ALS affected patient.
Tutor: MatteoMatteucci
Additional Info: CFU 2 - 20 / Master of Science, PhD / Thesis

Wiki Page: Real-time removal of ocular artifact from EEG
B bci.jpg

Title: Real-time removal of ocular artifact from EEG
Description: In a Brain-Computer Interface (BCI) based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

Tutor: MatteoMatteucci
Additional Info: CFU 2.5 - 5 / Bachelor of Science, Master of Science / Course

Past Projects


Useful Resources

  • AirBAT is a repository containing software developed in projects related to this area. See instructions here.