  Electroencephalography (EEG) is an extensively-used and well-studied
technique in the field of medical diagnostics and treatment for brain
disorders, including epilepsy, migraines, and tumors. The analysis and
interpretation of EEGs require physicians to have specialized training, which
is not common even among most doctors in the developed world, let alone the
developing world where physician shortages plague society. This problem can be
addressed by teleEEG that uses remote EEG analysis by experts or by local
computer processing of EEGs. However, both of these options are prohibitively
expensive and the second option requires abundant computing resources and
infrastructure, which is another concern in developing countries where there
are resource constraints on capital and computing infrastructure. In this work,
we present a cloud-based deep neural network approach to provide decision
support for non-specialist physicians in EEG analysis and interpretation. Named
`neurology-as-a-service,' the approach requires almost no manual intervention
in feature engineering and in the selection of an optimal architecture and
hyperparameters of the neural network. In this study, we deploy a pipeline that
includes moving EEG data to the cloud and getting optimal models for various
classification tasks. Our initial prototype has been tested only in developed
world environments to-date, but our intention is to test it in developing world
environments in future work. We demonstrate the performance of our proposed
approach using the BCI2000 EEG MMI dataset, on which our service attains 63.4%
accuracy for the task of classifying real vs. imaginary activity performed by
the subject, which is significantly higher than what is obtained with a shallow
approach such as support vector machines.
