EMAP
A Cloud-Edge Framework for EEG Monitoring and Real-time Anomaly Pred.
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 an anomaly. Using the proposed framework, we have demonstrated a prediction accuracy of up to 94% for the three different anomalies that we have tested.
This work was published and presented at Design Automation Conference...