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Developing a Brain-Computer Interface~(BCI) for seizure prediction can helpepileptic patients have a better quality of life. However, there are manydifficulties and challenges in developing such a system as a real-life supportfor patients. Because of the nonstationary nature of EEG signals, normal andseizure patterns vary across different patients. Thus, finding a group ofmanually extracted features for the prediction task is not practical. Moreover,when using implanted electrodes for brain recording massive amounts of data areproduced. This big data calls for the need for safe storage and highcomputational resources for real-time processing. To address these challenges,a cloud-based BCI system for the analysis of this big EEG data is presented.First, a dimensionality-reduction technique is developed to increaseclassification accuracy as well as to decrease the communication bandwidth andcomputation time. Second, following a deep-learning approach, a stackedautoencoder is trained in two steps for unsupervised feature extraction andclassification. Third, a cloud-computing solution is proposed for real-timeanalysis of big EEG data. The results on a benchmark clinical datasetillustrate the superiority of the proposed patient-specific BCI as analternative method and its expected usefulness in real-life support of epilepsypatients.

Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction