Аннотации:
A Brain-Computer Interface (BCI) is a continuously evolving technological framework
that has been steadily gaining popularity over the past few decades. By recording the
brain activity and structure through various means like electrical potential recording,
Magnetic-Resonance Imaging (MRI), or even Near-Infrared Spectroscopy (NIRS),
BCIs allow us to use that data for communication between a human and an external
computing device. This leads to a very wide range of possible applications, such
as contributing to rehabilitation, control of a prosthesis, and managing/diagnosing
disorders such as Attention-Deficit Hyperactivity Disorder (ADHD). However, the
creation of a fast yet reliable BCI model remains one of the biggest challenges even
today. Main complications also include the performance and testing of different BCI
models on datasets with strong spatial smearing (noise), along with the general problem
of non-linear classifiers being intractable (black-box). Hence, the following study
tries to cover the aforementioned problems. First of all, a general analysis of existing
BCI models on multiple BCI datasets is given, followed by a proposal of a custom Deep
Learning architecture with a performance comparable to state-of-the-art BCI classifiers.
Secondly, the practical feasibility of Layerwise Relevance Propagation (LRP) in
the field of BCI is later explored. Knowing the reasoning behind a model feature selection
may lead to novel insights with respect to neuroplasticity and subject-to-subject
analysis. Furthermore, the study investigates the pruning potential of the LRP, showcasing
an efficient removal of unnecessary network complexity in the model. Finally,
the study also discusses some ideas for the further development and testing of BCI
systems, including showcasing the practical feasibility and construction of a virtual
environment for prosthesis training and patient rehabilitation.