Self Aware Epilepsy Monitoring
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, sensations, and sometimes loss of awareness. The golden diagnostic standard is represented by Electroencephalography (EEG) systems, which unfortunately are cumbersome and can make patients uncomfortable because of perceived stigmatization. Thus, both patients and caregivers would benefit from the availability of wearable long-term EEG monitoring devices. These long-term EEG monitoring devices must be robust to different noises or artifacts, which can be either external disturbances or movement of the patient that taints the EEG signal.
In this project, the student will work on designing and implementing a self-aware machine learning model to be used in a wearable system for real-time detection of epileptic seizures. The self-aware part of the system will be implemented in a way where the quality of the data is assessed before feeding it into a classifier: if the system is certain of good quality data that should be easy to classify, a more simple and energy-efficient classifier is used; if, instead, input data guarantee low confidence, a more complex classifier can be employed.
The resulting model/s will then be implemented on a real microcontroller, and performance and power measurements will be performed.
- Looking for Semester and Master Project Students
- Supervision: Thorir Mar Ingolfsson, Andrea Cossettini, Simone Benatti
- 20% literature review
- 80% Implementation
- V. Kartsch et. al., BioWolf: A Sub-10-mW 8-Channel Advanced Brain–Computer Interface Platform With a Nine-Core Processor and BLE Connectivity