Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

Abstract

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

Figure 1

Current generation of Mercury robotic arm: (a) the robotic arm in position during an illustrative experiment, (b) the 3D-printed gripper (in focus circle), and (c) schematic of the 8 DoFs of the robotic arm. Mercury arms are house-built, of low cost, and anthropomorphic.

Figure 2

Schematic of Brain-Computer Interface loop: using off-the-shelf EEG-BCI for control of house-built robotic arms.

Figure 3

Overview of the experimental setup in the Thess-AHALL Living Lab. The figure is modified with authors' permission [46].

Figure 4

The training procedure of the qualitative assessment experiment.

Figure 5

Overview of robotic arm control trials during the qualitative assessment experiment.

Figure 6

28-year-old female SCI patient participating in the pilot investigation: (a) 1st part of the experiment, 128-channel EEG recording during oddball presentation of multiple limb movements (visual imagery); (b) 2nd part of the experiment, control of robotic arms using a commercial EEG-BCI headset, employing mental rehearsal of movements (kinesthetic imagery).

Figure 7

Regions of interest (ROIs) for connectivity analysis at the cortical level: (a) midline surface, left hemisphere, (b) top view, both hemispheres, and (c) lateral view, right hemisphere. (1): SAC, (2): S1F, (3): S1H, (4): S2, (5): CMA, (6): M1F, (7): M1H, (8): M1L, (9): SMA, (10): pSMA, (11): PMd, and (12): PMv.

Figure 8

KMI against VMI success scores for 24 participants above action power threshold. Most participants performed better with VMI but the difference was not statistically significant.

Figure 9

Performance in BCI control of patient and healthy participants in comparison to (a) their g-SCIM-III total score, (b) VVIQ score, and (c) Godspeed total score. Also (d) evaluation of the robotic arms in each separate Godspeed subcategory by participant.

Figure 10

Functional connectivity networks formed in alpha brainwave band during different visual motor imagery tasks performed by an SCI patient and a sex and age matched healthy control participant (connections > 60% max power displayed).