Predictive Shared Control of Robotic Arms Using Simulated Brain-Computer Interface Inputs

Abstract

Low decoding accuracy makes brain-computer interface (BCI) control of a robotic arm difficult. Shared control (SC) can overcome limitations of a BCI by leveraging external sensor data and generating commands to assist the user. Our study explored whether reaching targets with a robot end-effector was easier using SC rather than direct control (DC). We simulated a motor imagery BCI using a joystick with noise introduced to explicitly control interface accuracy to be 65% or 79%. Compared to DC, our prediction-based implementation of SC led to a significant reduction in the trajectory length of successful reaches for 4 (3) out of 5 targets using the 65% (79%) accurate interface, with failure rates being equivalent to DC for 2 (1) out of 5 targets. Therefore, this implementation of SC is likely to improve reaching efficiency but at the cost of more failures. Additionally, the NASA Task Load Index results suggest SC reduced user workload.Clinical relevance-Shared control can minimise the impact of BCI decoder errors on robot motion, making robotic arm control using noninvasive BCIs more viable.