Semi-Autonomous Personal Care Robots Interface Driven by EEG Signals Digitization

In this paper, we propose an innovative architecture that merges the Personal Care Robots (PCRs) advantages with a novel Brain Computer Interface (BCI) to carry out assistive tasks, aiming to reduce the burdens of caregivers. The BCI is based on movement related potentials (MRPs) and exploits EEG from 8 smart wireless electrodes placed on the sensorimotor area. The collected data are firstly pre-processed and then sent to a novel Feature Extraction (FE) step. The FE stage is based on symbolization algorithm, the Local Binary Patterning, which adopts end-to-end binary operations. It strongly reduces the stage complexity, speeding the BCI up. The final user intentions discrimination is entrusted to a linear Support Vector Machine (SVM). The BCI performances have been evaluated on four healthy young subjects. Experimental results showed a user intention recognition accuracy of ~84 % with a timing of ~ 554 ms per decision. A proof of concept is presented, showing how the BCI-based binary decisions could be used to drive the PCR up to a requested object, expressing the will to keep it (delivering it to user) or to continue the research.