University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Deep neural architectures for mapping scalp to intracranial EEG

Antoniades, Andreas, Spyrou, Loukianos, Martin-Lopez, David, Valentin, Antonio, Alarcon, Gonzalo, Sanei, Saeid and Cheong Took, Clive (2018) Deep neural architectures for mapping scalp to intracranial EEG International Journal of Neural Systems, 28 (8), 1850009.

Deep neural architectures for mapping scalp to intracranial EEG.pdf - Accepted version Manuscript

Download (446kB) | Preview


Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and EEG data is no exemption. Intracranial EEG data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for non-linearly mapping scalp to intracranial EEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp- intracranial recording to establish a novel methodology for detecting the epileptic discharges from the scalp EEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real world clinical application, these pseudo-intracranial EEG are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of intracranial EEG and the limitations of scalp EEG, we have achieved a classification accuracy of 64%; an increase of 6% over the previously proposed linear regression mapping.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Spyrou, Loukianos
Martin-Lopez, David
Valentin, Antonio
Alarcon, Gonzalo
Cheong Took,
Date : 10 April 2018
DOI : 10.1142/S0129065718500090
Copyright Disclaimer : Copyright © 2018 World Scientific Publishing Co Pte Ltd
Uncontrolled Keywords : Interictal epileptic discharge; Scalp to intracranial EEG mapping; Asymmetric deep learning
Related URLs :
Depositing User : Clive Harris
Date Deposited : 28 Feb 2018 14:47
Last Modified : 27 Feb 2019 02:08

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800