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Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

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Understanding intention of movement from electroencephalograms

Lakany, H. and Conway, B.A. (2007) Understanding intention of movement from electroencephalograms. Expert Systems, 24 (5). pp. 295-304. ISSN 0266-4720

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Abstract

In this paper, we propose a new framework for understanding intention of movement that can be used in developing non-invasive brain-computer interfaces. The proposed method is based on extracting salient features from brain signals recorded whilst the subject is actually (or imagining) performing a wrist movement in different directions. Our method focuses on analysing the brain signals at the time preceding wrist movement, i.e. while the subject is preparing (or intending) to perform the movement. Feature selection and classification of the direction is done using a wrapper method based on support vector machines (SVMs). The classification results show that we are able to discriminate the directions using features extracted from brain signals prior to movement. We then extract rules from the SVM classifiers to compare the features extracted for real and imaginary movements in an attempt to understand the mechanisms of intention of movement. Our new approach could be potentially useful in building brain-computer interfaces where a paralysed person could communicate with a wheelchair and steer it to the desired direction using a rule-based knowledge system based on understanding of the subject's intention to move through his/her brain signals.