Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential

Abstract

Background and objectiveBrain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP. MethodsThe initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts. ResultsThe ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % ± 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types. ConclusionsThe results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.