Connectivity modeling meets machine learning: The next generation of eeg-based brain computer interfaces

This position paper discusses the definition and implementation of a Brain Computer Interface (BCI) system based on Brain Connectivity (BC) and Machine Learning (ML) in motor imagery applications. BCI has become a well-established technique to control external devices and consequently help people in their everyday life. During the years many approaches have been explored in terms of neurological information, feature extraction, signal processing, and intention prediction. Two novel aspects are becoming increasingly interesting for the BCI community, i.e. BC modeling and ML techniques. The former aims at describing the interactions among different brain regions as connectivity patterns that reflect the dynamics of information flow at rest or when performing a task. The latter is becoming pervasive for its capability of modeling and predicting complex scenarios where a huge amount of data is involved. These aspects are relevant when considered by themselves, but become crucial assets when combined together. In this scenario, our research idea consists in identifying how the information in the two domains can be merged in a whole entity under a theoretical point of view: BC and ML applied to Electroencephalography (EEG) signals could provide the generalization capability, high accuracy and minimal computational time that allow researchers to build a reliable BCI capable to operate online.