Daily usage of brain-controlled robots and neuroprostheses is the paramount promise of brain-machine interface (BMI) for people suffering from severe motor disabilities. A new study by the University of Texas at Austin takes a step forward for brain-machine interfaces — computer systems that turn mind activity into action.
In this study, several people with motor disabilities could operate a wheelchair that translates their thoughts into movement. The study is also significant because of the noninvasive equipment used to operate the wheelchair.
José del R. Millán, professor in the Cockrell School of Engineering’s Chandra Family Department of Electrical and Computer Engineering, who led the international research team, said, “We demonstrated that the people who will be the end users of these types of devices can navigate in a natural environment with the assistance of a brain-machine interface.”
The notion of a thought-powered wheelchair has been investigated for years. Still, most efforts have relied on non-disabled people or stimuli that cause the wheelchair to control the user instead of the other way around.
In this instance, three people with tetraplegia—the inability to move one’s arms and legs due to spinal injuries—operated the wheelchair to various degrees of success in a chaotic, natural setting. The interface captured their brain activity, and a machine-learning algorithm converted it into instructions for operating the wheelchair.
Scientists noted, “This is a sign of future commercial viability for mind-powered wheelchairs that can assist people with limited motor function.”
“The study is also significant because of the noninvasive equipment used to operate the wheelchair.”
Surprisingly, scientists did not implant any device into the participants or use any type of stimulation on them. Participants had to wear a cap with electrodes that recorded brain electrical activity, known as an electroencephalogram (EEG). These electrical signals were amplified and transferred to a computer, translating each participant’s thoughts into action.
Two important dynamics were major contributors to the success of the study. The first involves a training program for the users.
The techniques for visualizing moving the chair were taught to the users the same way they would have learned to move their hands and feet. The study participants’ brain activity changed as they gave commands, and the scientists were able to monitor these changes.
The second contributor borrowed from robotics. To better grasp their surroundings, the scientists equipped their wheelchairs with sensors. Additionally, they used robotic intelligence software to help the wheelchair travel accurately and safely by filling in the gaps in the users’ commands.
Millán said, “It works a lot like riding a horse. The rider can tell the horse to turn left or enter a gate. But the horse will ultimately have to figure out the optimal way to carry out those commands.”
Team members in the project include Luca Tonin of the University of Padova in Italy; Serafeim Perdikis of the University of Essex in the United Kingdom; Taylan Deniz Kuzu, Jorge Pardo, Thomas Armin Schildhauer, Mirko Aach and Ramón Martínez-Olivera of Ruhr-Universität Bochum in Germany; Bastien Orset of École polytechnique fédérale de Lausanne in Switzerland; and Kyuhwa Lee of the Wyss Center for Bio and Neuroengineering in Switzerland.
- Luca Tonin et al. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience. DOI: 10.1016/j.isci.2022.105418