# Entropy-based Motion Intention Identification for Brain-Computer Interface

## TL;DR

This paper introduces an entropy-based method for real-time detection of motion intention in Brain-Computer Interfaces, achieving high accuracy and early prediction of user intent from EEG signals.

## Contribution

It presents a novel entropy-based approach for continuous motion intention identification in BCIs, enabling early and reliable detection of user commands.

## Key findings

- Achieves 80% accuracy in real-time motion prediction at 8 Hz
- Detects motion intention over 1 second before muscular activation
- Effective in identifying Intentional Non-Control states

## 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 $80\% \pm 5\%$ of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of $76\% \pm 11\%$.