Deep Neural Architectures for Mapping Scalp to Intracranial EEG

Abstract

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