By translating customers’ ideas into mechanical instructions, a mind-controlled wheelchair might help a paralyzed particular person achieve new mobility. Researchers exhibit that tetraplegic customers can function mind-controlled wheelchairs in a pure, cluttered surroundings after coaching for an prolonged interval in a examine printed immediately (November 18) within the journal iScience.
“We present that mutual studying of each the consumer and the brain-machine interface algorithm are each necessary for customers to efficiently function such wheelchairs,” says José del R. Millán, the examine’s corresponding writer at The College of Texas at Austin. “Our analysis highlights a possible pathway for improved scientific translation of non-invasive brain-machine interface know-how.”
Millán and his colleagues recruited three tetraplegic folks for the longitudinal examine. Every of the individuals underwent coaching periods 3 times per week for two to five months. The individuals wore a skullcap that detected their mind actions by electroencephalography (EEG), which might be transformed to mechanical instructions for the wheelchairs through a brain-machine interface gadget. The individuals had been requested to manage the course of the wheelchair by eager about shifting their physique components. Particularly, they wanted to consider shifting each fingers to show left and each ft to show proper.
This video exhibits a participant working a mind-controlled wheelchair throughout a cluttered room. Credit score: Luca Tonin
Within the first coaching session, three individuals had comparable ranges of accuracy—when the device’s responses aligned with users’ thoughts—of around 43% to 55%. Over the course of 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 there is a cortical reorganization that happened as a result of 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 only 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 are 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 figure out 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.
Reference: “Learning to control a BMI-driven wheelchair for people with severe tetraplegia” by Tonin and Perdikis et al., 18 November 2022, iScience.
This work was partially supported by the Italian Minister for Education and by the Department of Information Engineering of the University of Padova.