The project focuses on energy efficient Electroencephalography (EEG) monitoring of the brain activity exploiting subcutaneous implanted sensors. Exploiting such sensors will avoid the use of an EEG headset on the skull, which stigmatizes the user/patient of an EEG monitoring device. Also, exploiting such sensors allows, for instance, the use of a baseball-cap, hair-band, or even lighter head-worn fashion to enable EEG monitoring. The worn EEG monitoring device (hidden for example in a baseball-cap) should provide energy to the subcutaneous sensors and enable read out of a much better quality EEG signals. In the case of a subcutaneous sensors an RF field can be used to provide energy to the sensors since this implantable sensors reside closely to the skin. The EEG monitoring device described above is an energy-autonomous Medical Embedded System (MES). The goal of this project is to develop generic methods and tools for the design and programing of energy-autonomous MES. Energy autonomous systems either at start-up have sufficient energy capacity for the whole operational life of the system, or they need energy scavenging mechanisms to harvest energy during its operational lifetime. Furthermore, the processing and communication of the collected medical data during monitoring needs to be aware of the available energy. It is apparent that all medical data processing and communication in such energy-autonomous MES needs to be performed at the lowest possible energy cost in order to guarantee long autonomous operational lifetime.
Granted STW Project: Energy Efficient Computer-Brain Interaction
The STW project Energy Efficient Computer-Brain Interaction (principal investigator for LIACS: dr. T.P. Stefanov) has been granted. Funding for LIACS: 1 PhD student + travel/equipment budget, project duration: 4 years.