Automatic stimuli classification from ERP data for Brain-Computer Interfaces application

This work presents a supervised machine-learning approach to build an expert system that provides support to the neuroscientist in automatically classifying ERP data and matching them with a multisensorial alphabet of stimuli. To do this, two different approaches are considered: a hierarchical tree-based algorithm, XGBoost, and feedfoward neural networks, highlighting the pros and cons of both approaches in the different steps of the classification task. Moreover, the sensitivity of the classification capabilities of the tool as a function of the number of available electrodes is also studied, highlighting what can be achieved by applying the method using commercial, wearable EEG systems. The main novelty of this work consists in significantly enlarging the pool of stimuli that the expert system can recognize and comprising different, possibly mixed, sensorial domains. The obtained results open the way to the design of portable devices for augmented communication systems, which can be of particular interest for the development of advanced Brain–Computer Interfaces (BCI) for communication with different types of neurologically impaired patients.