  State-of-the-art techniques for detecting, or predicting, neurological
disorders (1) focus on predicting each disorder individually, and are (2)
computationally expensive, leading to a delay that can potentially render the
prediction useless, especially in critical events. Towards this, we present a
real-time two-tiered framework called EMAP, which cross-correlates the input
with all the EEG signals in our mega-database (a combination of multiple EEG
datasets) at the cloud, while tracking the signal in real-time at the edge, to
predict the occurrence of a neurological anomaly. Using the proposed framework,
we have demonstrated a prediction accuracy of up to 94% for the three different
anomalies that we have tested.
