Deep neural architectures for mapping scalp to intracranial EEG

Antoniades, A, Spyrou, L, Martin-Lopez, D, Valentin, A, Alarcon, G, Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801 and Took, CC, 2018. Deep neural architectures for mapping scalp to intracranial EEG. International Journal of Neural Systems, 28 (8): 1850009. ISSN 0129-0657

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Abstract

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) 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 nonlinearly mapping scalp to iEEG 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 sEEG 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-iEEGs 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 iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

Item Type:	Journal article
Publication Title:	International Journal of Neural Systems
Creators:	Antoniades, A., Spyrou, L., Martin-Lopez, D., Valentin, A., Alarcon, G., Sanei, S. and Took, C.C.
Publisher:	World Scientific Publishing
Date:	2018
Volume:	28
Number:	8
ISSN:	0129-0657
Identifiers:	Number Type 10.1142/S0129065718500090 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	10 May 2018 11:05
Last Modified:	19 Jul 2019 09:44
URI:	https://irep.ntu.ac.uk/id/eprint/33518

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