Talk:Feature Selection and Extraction for a BCI based on motor imagery

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Part 1: Project profile

Project name

Feature Selection and Extraction for a BCI based on motor imagery

Project short description

The aim of this project is to explore and analyze methods, pointed out from literature, for feature selection and extraction in a BCI system based on motor imagery.

Dates

Start date: 0?/0?/2008

End date:

People involved

Project head(s)
Other Politecnico di Milano people
Students currently working on the project

Laboratory work and risk analysis

Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line. Standard safety measures described in Safety norms will be followed.

Part 2: Project description

L'obbiettivo principale che questo progetto si prefigge è l'analisi di algoritmi di filtraggio spaziale applicati in un sistema BCI basato su Motor Imagery. L'obbiettivo specifico dell'analisi è la valutazione dell'impatto dei suddetti algoritmi sulle capità di classificazione del sistema attualmente in fase di sviluppo presso IIT-Lab. Selection of algorithm to analize was made looking for ... inside litterature and also BCI Competition' results.

Brief description of Motor Imagery BCI architecture

Even if all BCI system share the same goal, create a new communication patway from mind to an external device, this isn't always true for architecture, due to different type of brian signal observed. However, we could trace a common design shared from most BCI application. Project work was focus on Signal Processing block, more specifically on Featur Filtering subblock.

Analysis Tool

We will develop an offline analysis tool on Matlab to generate spazial filter starting from data recorded with BCI2000 ... Tool will also provide functionality to test filter and visualize filter application result.


ICA

Independent Component Analysis is a blind source separation tecnique applied to recover sorce from a mixture signal observation. ICA algorithm emploies statistical mutal indipendence of component to extract soruce signal from mixture one. We found different algorithm depending on contrast funcion used to maximize independence. We analize two ICA algorithm, Infomax adn FASTICA, whom seems to have good reults when applied to BCI data.

Infomation maximization algorithm, also kwon as Infomax, was based on approach developed from Bell & Sejnowski, that find a solution maximization the joint entropy of the output H(y). An implementation fo this algorithm was found... Algorithm have many parameter:

  • 'learning rate'
  • max step
  • momentum
  • [bias]