Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Spyrou, L., Kouchaki, S. and Sanei, S. ORCID: 0000-0002-3437-2801, 2018. Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation. Journal of Signal Processing Systems, 90 (2), pp. 273-284. ISSN 1939-8018

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Abstract

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy.

Item Type: Journal article
Publication Title: Journal of Signal Processing Systems
Creators: Spyrou, L., Kouchaki, S. and Sanei, S.
Publisher: Springer
Date: February 2018
Volume: 90
Number: 2
ISSN: 1939-8018
Identifiers:
NumberType
10.1007/s11265-016-1164-zDOI
1164Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 12 Feb 2018 12:39
Last Modified: 25 Jul 2018 15:18
URI: http://irep.ntu.ac.uk/id/eprint/32673

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