EMAP

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 2020 (DAC 2020).

In case of usage please refer to:
B. S. Prabakaran, A. G. Jiménez, G. M. Martínez, M. Shafique, “EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction”, IEEE/ACM 57th Design Automation Conference (DAC), July, 2020, (Accepted).