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# Mind control robots

Mind control robots . All materials are based on the following paper

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## Mind control robots

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1. Mind control robots All materials are based on the following paper Meel Velliste, Sagi Perel, M. Chance Spalding, Andrew S. Whitford and Andrew B. Schwartz,Cortical control of a prosthetic arm for self-feeding,Nature,doi:10.1038/nature06996; Received 14 November 2007; Accepted 4 April 2008 (2008)

2. In May, researchers at the University of Pittsburgh said they had taught two monkeys to grab small amounts of food with a mechanical arm using their brains. • Video 1: http://www.youtube.com/watch?v=wxIgdOlT2cY&feature=related • Video 2: (with researcher’s explanation) • http://www.youtube.com/watch?v=iys5wvQD72Y&feature=related

3. How they do it: • 1. use brain signals: record signals from motor cortex • 2. pull out wires to transfer signals to the system (PVA) • 3. in the system, computers will decode what the money want to do • 4. drive the arm to the target.

4. Here are the key points • 1. how to get the signals precisely • 2. how to decode the signals by the compute (PVA) • 3. how to use these decoded information to control a robot arm and do what the money wants to do.

5. How to get the signals: PVA • Modulation in motor cortical neuron firing rate often has an almost linear relationship to movement kinematics. Therefore, linear equations are commonly used to describe expected cell behavior. • Population Vector Algorithm: • For a single cell:

6. The cosine tuning function is wide, encompassing all movement directions. But the peak of the tuning function is used to categorize each cell’s contribution to the ensemble’s movement representation in the population vector algorithm (PVA). The ith contribution, Ci, to the population output is represented as a unit vector pointing in its prefered direction, and weight by some function of its firing rate:

7. The weighted cell vector are then summed across all cells to form the population vector, P, which points in the predicted direction of movement:

8. Again: • Monkeys learned a continuous self-feeding task involving real-time physical interaction between the arm, a food target, a presentation device(designed to record the target’s three-dimensional location) and their mouth. • Each monkey controlled the arm and gripper continuously during an entire session. • The task was challenging because the positional accuracy required. (about 5 - 10 mm from the target center position at the time of gripper closing)

9. The task was challenging because the positional accuracy required. (about 5 - 10 mm from the target • Figure 1: behavioural paradigm • a: embodied control setup. Each monkey had its arms restrained(inserted up to the elbow in horizontal tubes), and a prosthetic arm positioned next to its shoulder. Spiking activity was processed and used to control the 3d arm velocity and gripper aperture velocity in real time. Food targets were presented. • b: timeline of trial periods during the continuous self-feeding task. Each trial started with presentation of a food piece, and a successful trial ended with the monkey unloading(UL) the food from the gripper into its mouth. Note: there’s no clear boundaries between the task periods.

10. Spike rasters of 116 units used for control. Rows represents spike occurrences for each unit, grouped by major tuning component(red, x; green, y; blue, z; purple, gripper) • b-d. the x, y and z component, respectively of robot end point position. Grey background indicates inter-trial intervals. Arrows indicate gripper closing at target. • e. Gripper command aperture.(0: closed, 1: open) • f. Spatial trajectories for the same four trials. Color indicates gripper aperture(blue, closed; purple, half-closed; red, open). Arrows indicate movement direction. • g. Distribution of the 4-dimensional preferred directions of the 116 units used. Arrow direction indicates x, y, z components, color indicates gripper component(blue, negative; purple, zero; red, positive) Figure 2: unfiltered kinematic and spike data

11. From fig. 2e: gripper opens and closes fully each time. It is good performance. (in early training session, the monkey is capable of partially opening or closing the gripper). • A surprising point from fig. 2f: after gripping the food and pulling it off the presentation device, the money gradually opened the gripper on the way back to mouth. This might cause the drop of food.

12. Figure 3: movement quality

13. From fig3 b: the animal controlled the exact path of the arm to achieve the correct approach direction to position the gripper in the precise location needed to grasp the food. The curved path is taken to avoid knocking the food piece off the presentation device. • There should be NO apparent control delay. • The delay between spike signals and movement of the robotic arm was about 150ms (not very different from the control delay of a natural arm.)

14. Figure 4. unit modulation a, Spike rasters of a single unit during six movements in each of eight directions. This unit (with {x,y,z} components of its preferred direction, PD5{20.52,0.21,0.47}) fired maximally in the backward-up-right direction (B,U,R) while retrieving from the lower left target, and fired least in the forward-downleft direction (F,D,L) while reaching to the same target. The modulation was consistent during (blue side bars) and after calibration (red side bars). b, Gripper modulation. Aperturecommand velocity (dotted line) and off-line predicted aperture velocity from neural data (solid line,62 standard errors) during automatic gripper control, showing that the monkey’s cortical population is modulated for observed gripper movement.