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Brain Computer Interfacing Brain Computer Interfacing

Brain Computer Interfacing - PowerPoint Presentation

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Brain Computer Interfacing - PPT Presentation

Uses of the campus Grid in Cybernetics Ian Daly Dr Slawomir J Nasuto Prof Kevin Warwick 17 th June 2009 What is a BCI BCIs allow control of a computer by thought alone Allows individuals with severe motor impairments greater levels of communication and environmental control ID: 358148

bci control grid speech control bci speech grid feature selection algorithms http www space signal computing desc html greater

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Slide1

Brain Computer Interfacing

Uses of the campus Grid in Cybernetics

Ian Daly,

Dr

Slawomir

J.

Nasuto

,

Prof. Kevin Warwick

17

th

June 2009Slide2

What is a BCIBCIs allow control of a computer by thought alone.Allows individuals with severe motor impairments greater levels of communication and environmental control.

Uses:Typing programs; Email, Text to speech, Twitter etc.Environment; Lighting, TV, Wheelchair control etc.

Games; Table tennis, bio-feedback etc.Prosthetics.Slide3

Types of BCIInvasive vs. Non-invasiveControl vs. Goal orientated

P300 basedERS / ERD basedMotor imagerySlide4

How it works

Stimuli presentation

Data recording

& pre-processing

Feature extraction

Training and classification

http://ida.first.fraunhofer.de/projects/bci/competition_ii/albany_desc/albany_desc_ii.html

http://www.musicandmeaning.net/issues/showArticle.php?artID=3.5

http://www.jvrb.org/archiv/760/index_html?set_language=en&cl=enSlide5

Our ResearchMachine learning and signal processingICA, EMD, HMMs, Phase synchronisationArtefact removal

Extraction of ERPs from single trialsAutomated feature selectionModels for simulated ERP generation.

New types of BCI paradigm– speech imageryAlternative hardware developmentSlide6

How we use Grid Computing (1)Speech imagery

Template method investigated for classification of speech related EEG.Large parameter space.

Multiple parameter subsets simultaneously evaluated on Condor.Quickly able to demonstrate that template method over simplifies signal variability.Slide7

How we use Grid Computing (2)

Feature selectionEEG can be described by an infinite number of different features.Feature selection algorithms - large search space.

GA’sSwarm intelligenceNovel algorithms...

Condor allows quick traversal of the search space of possible features.Slide8

The FutureNeed for newer / faster / more intuitive BCIsFaster, more efficient control and communicationGreater ease of use

More robust and reliableNew BCI paradigms and more efficient algorithms in development.Brain signal can be described in an infinite number of different ways.

Grid computing presents an effective way of investigating some of these possibilities.Slide9

Thank you for listeningQuestions?

www.ucdmc.ucdavis.edu