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Predicting numerical processing in naturalistic settings from controlled experimental conditions

Schrouff, J; Phillips, C; Parvizi, J; Mourao-Miranda, J; (2015) Predicting numerical processing in naturalistic settings from controlled experimental conditions. In: (Proceedings) 2015 International Workshop on Pattern Recognition in NeuroImaging PRNI 2015. (pp. pp. 81-84). IEEE Green open access

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Abstract

Machine learning research is interested in building models based on a training set that can then be applied to new data, whether this unseen data comes from new examples (e.g. New subjects, other tasks) or new features (e.g. Different modalities). In this work, we present a simple approach to transfer learning using intracranial EEG (also known as electrocorticographic, ECoG) data from three patients. More specifically, we aimed at detecting numerical processing during naturalistic settings based on a model trained with controlled experimental conditions. Our results showed significant prediction accuracy of numerical events in naturalistic settings when considering a priori knowledge of the target task.

Type: Proceedings paper
Title: Predicting numerical processing in naturalistic settings from controlled experimental conditions
Event: 2015 International Workshop on Pattern Recognition in NeuroImaging PRNI 2015
Location: Stanford, CA
Dates: 10 June 2015 - 12 June 2015
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PRNI.2015.13
Publisher version: https://doi.org/10.1109/PRNI.2015.13
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Artificial Intelligence, Neuroimaging, Computer Science, Neurosciences & Neurology, Electrocorticography, Transfer learning, Multiple Kernel Learning, NEUROIMAGING DATA
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1501985
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