The focus of the project is on next generation solutions for wearable and low-power electroencephalography (EEG) devices for long term non-invasive brain monitoring. While wearable brain monitoring devices have many relevant applications within disease monitoring, seizure detection, evaluation of treatment and medication efficacy, brain computer interfaces etc., there are a number of technical and scientific challenges to address for this to become real. This Ph.D. project is targeting some of the signal processing challenges. The project will establish theoretical and practical performance bounds for this type of devices, with respect to the signal and noise levels, number and arrangement of electrodes and the amount of signal processing required. The aim of the project is to develop novel signal processing algorithms for the above mentioned applications. The Ph.D. project is a part of a larger ongoing research project.
Holds a relevant M.Sc. degree with a strong theoretical basis within mathematics and signal processing. Are capable of doing practical measurements in the lab and solving the practical challenges that are an intrinsic part of working with experiments and test subjects. Have preferably practical experience with basic electrical engineering lab equipment, signal processing and Matlab. Have preferably knowledge within the field of electrophysiology/EEG.