MIndGrasp: A New Training and Testing Framework for Motor Imagery Based 3-Dimensional Assistive Robotic Control
This work addresses the challenge of making brain-computer interfaces more usable for disabled individuals needing assistive robotic control, though it is incremental as it builds on existing methods with simulation-based improvements.
The paper tackles the difficulty of controlling 3D assistive robots with motor imagery brain-computer interfaces by reducing the required motor imagery classes from 6 to 4, using a simulated framework with semi-autonomous vision-based control for object grasping.
With increasing global age and disability assistive robots are becoming more necessary, and brain computer interfaces (BCI) are often proposed as a solution to understanding the intent of a disabled person that needs assistance. Most frameworks for electroencephalography (EEG)-based motor imagery (MI) BCI control rely on the direct control of the robot in Cartesian space. However, for 3-dimensional movement, this requires 6 motor imagery classes, which is a difficult distinction even for more experienced BCI users. In this paper, we present a simulated training and testing framework which reduces the number of motor imagery classes to 4 while still grasping objects in three-dimensional space. This is achieved through semi-autonomous eye-in-hand vision-based control of the robotic arm, while the user-controlled BCI achieves movement to the left and right, as well as movement toward and away from the object of interest. Additionally, the framework includes a method of training a BCI directly on the assistive robotic system, which should be more easily transferrable to a real-world assistive robot than using a standard training protocol such as Graz-BCI. Presented results do not consider real human EEG data, but are rather shown as a baseline for comparison with future human data and other improvements on the system.