Fig. 1 Experimental setup. (A) Chair with a human-like robotic arm on its side. (B) Ball-balancing board containing color-shape markers. (C) For the single task, participants had to imagine the goal-oriented action of grasping or releasing a bottle with the robotic arm. (D) For the multitask, participants had to imagine the goal-oriented action of grasping the bottle while simultaneously balancing a ball on a board held with their own hands. Participants were asked to wear black gloves with long sleeves to avoid false-positive detections of the color markers by the camera during ball-balancing session.
Fig. 2 Histograms showing the distribution of people with respect to their performances for the single task and multitask conditions. (A) Single task. (B) Multitask. Panel (B) also shows a visual representation of the probability function resulting from the GMM-EM algorithm. The performance score (68.8) corresponding to the boundary between the two modalities was used to separate the two groups: good performers (above the boundary) and bad performers (below the boundary).
Supplementary Materials
robotics.sciencemag.org/cgi/content/full/3/20/eaat1228/DC1
Supplementary Text
Fig. S1. Trial description.
Fig. S2. System calibration.
Fig. S3. Robot arm configuration.
Fig. S4. Overall performance of all participants.
Fig. S5. Overall balancing performance.
Fig. S6. Balancing performance for good and bad performers.
Fig. S7. Frequency bands and channel locations.
Fig. S8. Post-experimental subjective evaluation.
Additional Files
Supplementary Materials
This PDF file includes:
- Supplementary Text
- Fig. S1. Trial description.
- Fig. S2. System calibration.
- Fig. S3. Robot arm configuration.
- Fig. S4. Overall performance of all participants.
- Fig. S5. Overall balancing performance.
- Fig. S6. Balancing performance for good and bad performers.
- Fig. S7. Frequency bands and channel locations.
- Fig. S8. Post-experimental subjective evaluation.
Files in this Data Supplement:
