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Paralyzed Navigate Using Mind-Controlled Wheelchairs

Published: 2022-11-19
Author: Cell Press | Contact: cell.com
Peer-Reviewed Publication: Yes | DOI: https://dx.doi.org/10.1016/j.isci.2022.105418
Additional References: Library of Mobility Aids and Devices Publications

Synopsis: Researchers demonstrate that tetraplegic users could operate mind-controlled wheelchairs in a natural, cluttered environment after training for an extended period. Tetraplegic people were recruited for the longitudinal study and underwent training sessions thrice weekly for 2 to 5 months. The participants wore a skullcap that detected their brain activities through electroencephalography (EEG), which would be converted to mechanical commands for the wheelchairs via a brain-machine interface device. By the end of the training, all participants were asked to drive their wheelchairs across a cluttered hospital room. They had to go around obstacles such as a room divider and hospital beds, which were set up to simulate the real-world environment.

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Definition

Tetraplegia

Tetraplegia is another term for Quadriplegia - they are the same condition. Tetraplegia is the inability to move the upper and lower parts of the body voluntarily. The areas of impaired mobility usually include the fingers, hands, arms, chest, legs, feet, and toes and may or may not include the head, neck, and shoulders. The paralysis may be flaccid or spastic. A loss of sensory function can present as an impairment or complete inability to sense light touch, pressure, heat, pinprick or pain, and proprioception. In spinal cord injuries, losing sensation and motor control is common - What is Quadriplegia and Paraplegia?

Main Digest

A mind-controlled wheelchair can help a paralyzed person gain new mobility by translating users' thoughts into mechanical commands. On November 18, in the journal iScience, researchers demonstrated that tetraplegic users could operate mind-controlled wheelchairs in a natural, cluttered environment after training for an extended period.

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"We show that mutual learning of both the user and the brain-machine interface algorithm are both important for users to operate such wheelchairs successfully," says José del R. Millán, the study's corresponding author at The University of Texas at Austin. "Our research highlights a potential pathway for improved clinical translation of non-invasive brain-machine interface technology."

Millán and his colleagues recruited three tetraplegic people for the longitudinal study. Each participant underwent training sessions thrice weekly for 2 to 5 months. The participants wore a skullcap that detected their brain activities through electroencephalography (EEG), which would be converted to mechanical commands for the wheelchairs via a brain-machine interface device. The participants were asked to control the direction of the wheelchair by thinking about moving their body parts. Specifically, they needed to think about moving both hands to turn left and both feet to turn right.

In the first training session, three participants had similar levels of accuracy when the device's responses aligned with users' thoughts of around 43% to 55%. Throughout the training, the brain-machine interface device team saw significant improvement in accuracy in participant 1, who reached an accuracy of over 95% by the end of his training. The team also observed an increase in accuracy in participant 3 to 98% halfway through his training before the team updated his device with a new algorithm.

The improvement seen in participants 1 and 3 is correlated with improvement in feature discriminancy, which is the algorithm's ability to discriminate the brain activity pattern encoded for "go left" thoughts from that for "go right." The team found that the better feature discrimnancy is not only a result of machine learning of the device but also learning in the brain of the participants. The EEG of participants 1 and 3 showed clear shifts in brainwave patterns as they improved accuracy in mind-controlling the device.

"We see from the EEG results that the subject has consolidated a skill of modulating different parts of their brains to generate a pattern for 'go left' and a different pattern for 'go right,'" Millán says. "We believe a cortical reorganization happened due to the participants' learning process."

Compared with participants 1 and 3, participant 2 had no significant changes in brain activity patterns throughout the training. His accuracy increased slightly during the first few sessions, which remained stable for the rest of the training period. It suggests machine learning alone is insufficient for successfully maneuvering such a mind-controlled device, Millán says

By the end of the training, all participants were asked to drive their wheelchairs across a cluttered hospital room. They had to go around obstacles such as a room divider and hospital beds, which were set up to simulate the real-world environment. Both participants 1 and 3 finished the task, while participant 2 failed to complete it.

"It seems that for someone to acquire good brain-machine interface control that allows them to perform relatively complex daily activity like driving the wheelchair in a natural environment, it requires some neuroplastic reorganization in our cortex," Millán says.

The study also emphasized the role of long-term training in users. Although participant 1 performed exceptionally at the end, he struggled in the first few training sessions as well, Millán says. The longitudinal study is one of the first to evaluate the clinical translation of non-invasive brain-machine interface technology in tetraplegic people.

Next, the team wants to determine why participant 2 didn't experience the learning effect. They hope to conduct a more detailed analysis of all participants' brain signals to understand their differences and possible interventions for people struggling with the learning process in the future.

This work was partially supported by the Italian Minister for Education and the Department of Information Engineering of the University of Padova. iScience, Tonin and Perdikis et al.: "Learning to control a BMI-driven wheelchair for people with severe tetraplegia."

Reference Source(s):

Paralyzed Navigate Using Mind-Controlled Wheelchairs | Cell Press (cell.com). Disabled World makes no warranties or representations in connection therewith. Content may have been edited for style, clarity or length.

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