Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things
A brain-computer interface (BCI) for seizure prediction provides a means of controlling epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected but yet can be defined sufficiently to be selectively influenced by strategically implanted electrodes. Challenges remain in offering real-time solutions with such technology because of the immediacy of electrographic ictal behavior. The nonstationary nature of electroencephalographic (EEG) and electrocorticographic (ECoG) signals results in wide variation of both normal and ictal patterns among patients. The use of manually extracted features in a prediction task is impractical and the large amount of data generated even among a limited set of electrode contacts will create significant processing delays. Big data in such circumstances not only must allow for safe storage but provide high computational resources for recognition, capture and real-time processing of the preictal period in order to execute the timely abrogation of the ictal event. By leveraging the potential of cloud computing and deep learning, we develop and deploy BCI seizure prediction and localization from scalp EEG and ECoG big data. First, a new method for epileptic seizure prediction and localization of the seizure focus is presented. Second, an extended optimization approach on existing deep-learning structures, Stacked Auto-encoder and Convolutional Neural Network (CNN), is proposed based on principle component analysis (PCA), independent component analysis (ICA), and Differential Search Algorithm (DSA). Third, a cloud-computing solution (i.e., Internet of Things (IoT)), is developed to define the proposed structures for real-time processing, automatic computing and storage of big data. The ECoG clinical datasets on 11 patients illustrate the superiority of the proposed patient-specific BCI as an alternative to current methodology to offer support for patients with intractable focal epilepsy.