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With increasing global age and disability assistive robots are becoming morenecessary, and brain computer interfaces (BCI) are often proposed as a solutionto understanding the intent of a disabled person that needs assistance. Mostframeworks for electroencephalography (EEG)-based motor imagery (MI) BCIcontrol rely on the direct control of the robot in Cartesian space. However,for 3-dimensional movement, this requires 6 motor imagery classes, which is adifficult distinction even for more experienced BCI users. In this paper, wepresent a simulated training and testing framework which reduces the number ofmotor imagery classes to 4 while still grasping objects in three-dimensionalspace. This is achieved through semi-autonomous eye-in-hand vision-basedcontrol of the robotic arm, while the user-controlled BCI achieves movement tothe left and right, as well as movement toward and away from the object ofinterest. Additionally, the framework includes a method of training a BCIdirectly on the assistive robotic system, which should be more easilytransferrable to a real-world assistive robot than using a standard trainingprotocol such as Graz-BCI. Presented results do not consider real human EEGdata, but are rather shown as a baseline for comparison with future human dataand other improvements on the system.

MIndGrasp: A New Training and Testing Framework for Motor Imagery Based 3-Dimensional Assistive Robotic Control