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    block this user David M. W. Powers Trusted member

    Professor / David.Powers@flinders.edu.au

    Flinders University, School of Computer Science, Engineering and Mathematics, Adelaide, South Australia
    KUB/Tilburg University, ITK/Institute for Language and Knowledge Technology, Tilburg, Brabant, Holland
    FB Informatik/Faculty of Computer Science, University of Kaiserslautern, Germany
    Telecom Paris/ENST, Paris, France
    Macquarie University, Sydney, NSW, Australia
    UNSW/University of New South Wales, Sydney, NSW, Australia
    Sydney University, Sydney, NSW, Australia
    Cardiff University, Linguistics Department, Cardiff, Wales, UK
    Beijing Municipal Lab for Multimedia & Intelligent Software, Beijing University of Technology, Beijing, China

    PSO-Based Dimension Reduction of EEG Recordings: Implications for Subject Transfer in BCI

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    Subject transfer is a growing area of research in EEG aiming to address the lack of having enough EEG samples required for BCI by using samples originating from individuals or groups of subjects that previously performed similar tasks. This paper investigates the feasibility of two frameworks for enhancing subject transfer through a 90%+ reduction of EEG features and electrodes using Particle Swarm Optimization (PSO). In the first framework, electrodes and features selected by PSO from individual subjects are com- bined into a single ”meta-mask” to be applied to the new subject. In the second framework, the preprocessed EEG of multiple subjects is concate- nated into a single ”super subject”, from which PSO selects electrodes and features for use on the new subject. The study is focused on finding the optimal mixture of subjects in either of the proposed frameworks in addition to investigating the impact of various electrode and features selections. The results indicate the important role of having an optimal mixture of expertise in the subjects’ data.

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    Description

    Title : PSO-Based Dimension Reduction of EEG Recordings: Implications for Subject Transfer in BCI
    Author(s) : Adham Atyabi, Martin H. Luerssen, David M.W. Powers
    Abstract : Subject transfer is a growing area of research in EEG aiming to address the lack of having enough EEG samples required for BCI by using samples originating from individuals or groups of subjects that previously performed similar tasks. This paper investigates the feasibility of two frameworks for enhancing subject transfer through a 90%+ reduction of EEG features and electrodes using Particle Swarm Optimization (PSO). In the first framework, electrodes and features selected by PSO from individual subjects are com- bined into a single ”meta-mask” to be applied to the new subject. In the second framework, the preprocessed EEG of multiple subjects is concate- nated into a single ”super subject”, from which PSO selects electrodes and features for use on the new subject. The study is focused on finding the optimal mixture of subjects in either of the proposed frameworks in addition to investigating the impact of various electrode and features selections. The results indicate the important role of having an optimal mixture of expertise in the subjects’ data.
    Keywords : EEG, BCI, Dimension Reduction, Brain Imaging

    Subject : Brain Imaging
    Area : Computer Science
    Language : English
    Year : 2013

    Affiliations Flinders University, School of Computer Science, Engineering and Mathematics, Adelaide, South Australia
    Beijing Municipal Lab for Multimedia & Intelligent Software, Beijing University of Technology, Beijing, China
    Journal : Neurocomputing

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    David's Peer Evaluation activity

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    • Australian Speech Science Infrastructure: An Audio-Video Speech Corpus of Australian English, Grant Number LE0989734 / Year 2009
    • From Talking Heads to Thinking Heads: A Research Platform for Human Communication Science , Grant Number TS0669874 / Year 2006
    • Heterodensity neuroimaging techniques for spatiotemporal identification and localization , Grant Number DP0988686 / Year 2009
    • Enhanced brain and muscle signal separation verified by electrical scalp recordings from paralysed awake humans, Grant Number DP110101473 / Year 2011

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