Robohub.org
 

Teaching a brain-controlled robotic prosthetic to learn from its mistakes

by
01 October 2015



share this:
Using a BCI the robot was able to find targets that the person could see but the robot could not

Using a BCI the robot was able to find targets that the person could see but the robot could not (Photo: Iturrate et al., 2015).

Brain-Machine Interfaces (BMIs) — where brain waves captured by electrodes on the skin are used to control external devices such as a robotic prosthetic — are a promising tool for helping people who have lost motor control due to injury or illness. However, learning to operate a BMI can be very time consuming. In a paper published in Nature Scientific Reports, a group from CNBI, EPFL and NCCR Robotics show how their new feedback system can speed up the training process by detecting error messages from the brain and adapting accordingly.

One issue that bars the use of BMIs in everyday life for those with disabilities is the amount of time required to train users, who must learn to modulate their thought processes before their brain signals are clear enough to control an external machine. For example, to move a robotic prosthetic arm, a person must actively think about moving their arm — a thought process that uses significantly more brainpower than the subconscious thought required to move a natural arm. Furthermore, even with extensive training, users are often not able to perform complex movements.

It has been observed, however, that the brain emits very different waves when it experiences success at controlling a BMI than when it experiences failure. With this in mind, the research team developed a new feedback system that records error signals from the brain (called ‘error-related potentials’, or ErrPs) and uses these to evaluate whether or not the correct movement has been achieved. The system then adapts the movement until it finds the correct one, becoming more accurate the longer it is in use.

Schematic diagram of the new system

In order to determine the ErrP, twelve subjects were asked to watch a machine perform 350 separate movements, where the machine was programmed to make the wrong movement in 20% of cases. This step took an average of 25 minutes. After this first training stage, each subject performed three experiments where they attempted to locate a specific target using the robotic arm. As expected, the time taken to locate a target reduced as the experiment continued.

Experimental scheme

Three experiments showed that a robot improved its ability to find the position of a fixed point using error-related brain activity. (Iturrate et al. 2015)

 

This new approach finds obvious applicability in the field of neuroprosthesis, particularly for those with degenerative neurological conditions who find that their requirements change over time. The system also has the potential to automatically adapt itself without the need for retraining or reprogramming.

Reference

I. Iturrate, R. Chavarriaga, L. Montesano, J.  Minguez and J. del R. Millán, “Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control,” Nature Scientific Reports, vol. 5, Article number: 13893, 2015. doi:10.1038/srep13893


If you liked this article, you may also be interested in:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.



tags: , , , , ,


NCCR Robotics





Related posts :



Tesla’s Optimus robot isn’t very impressive – but it may be a sign of better things to come

Musk has now unveiled a prototype of the robot, called Optimus, which he hopes to mass-produce and sell for less than US$20,000 (A$31,000).
04 October 2022, by

Bipedal robot achieves Guinness World Record in 100 metres

Cassie the robot, developed at Oregon State University, records the fastest 100 metres by a bipedal robot.
03 October 2022, by and

Breaking through the mucus barrier

A capsule that tunnels through mucus in the GI tract could be used to orally administer large protein drugs such as insulin.
02 October 2022, by

Women in Tech leadership resources from IMTS 2022

There’ve been quite a few events recently focusing on Women in Robotics, Women in Manufacturing, Women in 3D Printing, in Engineering, and in Tech Leadership. One of the largest tradeshows in the US is IMTS 2022. Here I bring you some resources shared in the curated technical content and leadership sessions.
29 September 2022, by and

MIT engineers build a battery-free, wireless underwater camera

The device could help scientists explore unknown regions of the ocean, track pollution, or monitor the effects of climate change.
27 September 2022, by

How do we control robots on the moon?

In the future, we imagine that teams of robots will explore and develop the surface of nearby planets, moons and asteroids - taking samples, building structures, deploying instruments.
25 September 2022, by , and





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association