Inferring Sensitive User Information Through Biosignals Using Deep Learning

The growing popularity of wearable Internet of Things (IoT) devices has led to significant security and privacy concerns. The health data that these devices collect can be used to infer private and sensitive user information. This is especially true for users of Brain-Computer Interfaces (BCI) which measures brain activity via Electroencephalography (EEG) signals, and sometimes muscle movement from Electromyography (EMG) signals. In this research, we demonstrate the security risks associated with connected BCI devices. Specifically, we explore how EEG and EMG signals can be intercepted and analyzed using deep learning techniques to decode sensitive input such as Pattern Lock on users mobile devices.