Daniele Paliotta
Computationally Empowered Learning Strategies for Non-Invasive Intracranial Brain-Computer Interfaces.
Rel. Francesco Paolo Andriulli. Politecnico di Torino, Master of science program in Computer Engineering, 2020
Abstract
A brain-computer interface (BCI) is a system that is able to acquire brain signals and translate them into an input for a control system. Typical applications of BCIs span several domains, including the rehabilitation of patients with severe motor disabilities, prosthetic control, and augmentation of virtual reality environments. Most widespread BCIs are based on data recorded with an electroencephalograph (EEG), a device where a set of electrodes is placed on a subject's scalp to record the electric potential. Scalp EEG is a non-invasive and relatively cheap solution that provides measurements of brain activity with high temporal resolution. On the other hand, EEG is known to suffer from poor spatial resolution.
This makes it far from trivial to reconstruct actual electrical activity inside the head volume, a technique known as EEG source imaging (ESI)
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