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Brain-Computer Interface: Advancements and Applications in Cybernetics

This presentation discusses the fascinating world of Brain-Computer Interfaces (BCIs) and their applications, particularly in assisting individuals with severe motor impairments. BCIs enable direct control of computers using thought, facilitating typing, environmental control, and even gaming. The talk covers different types of BCIs, including invasive and non-invasive methods, and various paradigms like P300 and motor imagery. Furthermore, it highlights the use of grid computing for optimizing machine learning and feature selection, paving the way for faster and more intuitive BCIs.

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Brain-Computer Interface: Advancements and Applications in Cybernetics

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Presentation Transcript

  1. Brain Computer Interfacing Uses of the campus Grid in Cybernetics Ian Daly, Dr Slawomir J. Nasuto, Prof. Kevin Warwick 17th June 2009

  2. 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. • Uses: • Typing programs; Email, Text to speech, Twitter etc. • Environment; Lighting, TV, Wheelchair control etc. • Games; Table tennis, bio-feedback etc. • Prosthetics.

  3. Types of BCI • Invasive vs. Non-invasive • Control vs. Goal orientated • P300 based • ERS / ERD based • Motor imagery

  4. How it works • Stimuli presentation • Data recording • & pre-processing • Feature extraction • Training and classification http://www.musicandmeaning.net/issues/showArticle.php?artID=3.5 http://www.jvrb.org/archiv/760/index_html?set_language=en&cl=en http://ida.first.fraunhofer.de/projects/bci/competition_ii/albany_desc/albany_desc_ii.html

  5. Our Research • Machine learning and signal processing • ICA, EMD, HMMs, Phase synchronisation • Artefact removal • Extraction of ERPs from single trials • Automated feature selection • Models for simulated ERP generation. • New types of BCI paradigm– speech imagery • Alternative hardware development

  6. 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.

  7. How we use Grid Computing (2) • Feature selection • EEG can be described by an infinite number of different features. • Feature selection algorithms - large search space. • GA’s • Swarm intelligence • Novel algorithms... • Condor allows quick traversal of the search space of possible features.

  8. The Future • Need for newer / faster / more intuitive BCIs • Faster, more efficient control and communication • Greater ease of use • More robust and reliable • New 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.

  9. Thank you for listening Questions? www.ucdmc.ucdavis.edu

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