Entropy-based Motion Intention Identification for Brain-Computer Interface

2019/05/24 by Stefano, Tortora, Gloria, Beraldo, Luca, Tonin +1
#FOS: Computer and information sciences #Human-Computer Interaction (cs.HC)

paper · doi:10.48550/arxiv.1905.10254

Abstract

The identification of intentionally delivered commands is a challenge in Brain Computer Interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb prosthesis) are capable of detecting if the user is in the so called Intentional Non-Control (INC) state (i.e., holding the prosthesis in a given position). In this work, we propose a novel approach based on the entropy of the Electroencephalogram (EEG) signals to provide a continuous identification of motion intention. Results from ten healthy subjects suggest that the proposed system can be used for reliably predicting motion in real-time at a framerate of 8 Hz with 80% ± 5% of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of 76% ± 11%.

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