Abstract

Motor imagery (MI) is a fundamental brain-machine interface (BMI) paradigm in which users learn how to modulate their brain signals to voluntarily and selectively activate specific areas of the sensorimotor cortex. The self-paced nature of this kinesthetic imagination makes MI well-suited for several human-robot interaction (HRI) scenarios. However, the performance of MI decoding is highly dependent on both the user's expertise and the quality of the decoding algorithm. To address these challenges, we propose a method that integrates environmental data from robotic sensors to enhance the MI decoding process. Preliminary experiments indicate that this approach improves decoding accuracy and the overall performance of the brain-driven system, opening new opportunities for research on how machines can enhance the usability of BMIs systems.