Electroencephalography (EEG) is a fundamental clinical test used for assessing the conditions of the brain, recording electrical activity by a set of electrodes attached to the scalp. While traditional EEGs last a few tens of minutes, long-term EEGs last several hours or even days. In the last few years, long-term EEG has become more popular because of its importance for diagnosing and managing epilepsy or other neurological disorders. To achieve effective results, long-term EEGs should be performed outside clinical facilities, thus capturing the patient's conditions during the activities of daily living.

For remote EEG monitoring, it is necessary to implement ad hoc architectures combining wearable devices and Body Sensor Networks (BSNs) for processing the signals. The main challenge is tuning the number of sensors used for signal acquisition, which, in the case of EEG, can go from 15 to 64 electrodes or more. To provide a usable technology, the system design should minimize the number. On the other hand, a lightweight acquisition system possibly leads to suboptimal results from a clinical point of view.

This project aims to design new algorithms for processing EEG signals. The goal is to obtain an acquisition setup with a limited number of electrodes that still enables the reconstruction of a full EEG at the receiver. The project involves the design of new algorithms for reconstructing a full EEG and the analysis of the trade-off between the reconstruction accuracy and the amount of input information. To evaluate the final performance, it will consider both the mathematical difference between the original and the reconstructed signals and the signal's clinical evaluation, assessed in terms of the probability of detecting relevant clinical features.

For more information, contact Federico Mason at federico.mason@unipd.i