Brain Activity Recognition using EEG Signal
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. In this project, we propose an approach to interpret EEG signals for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to refine the raw EEG signals by eliminating various artifacts.
This work appears in MobiQuitous 2017.