Researchers demonstrate that tetraplegic users can operate mind-controlled wheelchairs in a natural, cluttered environment.
Everyone goes through an age where they wish they had power to control things with their mind. Human mind is believed to have such power hidden in it and what if we could get a hold of it?
Researchers at the University of Texas have developed a system that takes direct inputs from the human brain through Brain Machine Interface (BMI) and uses it as commands to drive the wheelchair. “We show that mutual learning of both the user and the brain-machine interface algorithm are both important for users to successfully operate such wheelchairs,” says José del R. Millán, the study’s corresponding author at The University of Texas at Austin.
Researchers recruited three tetraplegic people and trained them for 2-5 months. Users 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.
This video shows a participant operating a mind-controlled wheelchair across a cluttered room. Credit: Luca Tonin
Initial training sessions recorded around 43% to 55% accuracy for each user. Over the course of training, the brain-machine interface device team saw significant improvement in accuracy of over 95% by the end of his training. The team found that the better feature discriminancy 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.
“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.
Researchers 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.
Reference: “Learning to control a BMI-driven wheelchair for people with severe tetraplegia” by Tonin and Perdikis et al., 18 November 2022, iScience.
DOI: 10.1016/j.isci.2022.105418
