With training, people in mind-controlled wheelchairs can navigate normal, crowded places – ScienceDaily


A mind-controlled wheelchair could help a paralyzed person gain new mobility by translating the users’ thoughts into mechanical commands. On November 18 in the magazine iScienceresearchers show that quadriplegic users can operate mind-controlled wheelchairs in a crowded natural environment after training for a long time.

“We show that mutual learning of both the user interface algorithm and the brain and machine are important for users to operate these wheelchairs successfully,” says Jose Del R. Milan, corresponding study author at the University of Texas at Austin. “Our research highlights a potential path to improving clinical translation of this non-invasive brain-machine interface technology.”

Milan and colleagues recruited three quadriplegics for a longitudinal study. Each of the participants underwent training sessions three times a week for a period of 2 to 5 months. Participants wore a headgear that detects brain activities through electroencephalography (EEG), which will be converted into mechanical commands for wheelchairs via a brain-machine interface device. Participants were asked to control the direction of the wheelchair by thinking about the movement of their body parts. Specifically, they needed to consider 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 match the users’ thoughts—of about 43% to 55%. During the training period, the brain-machine interface device group experienced a significant improvement in accuracy in Participant 1, who had reached over 95% accuracy by the end of his training. The team also saw Participant 3’s accuracy increase from 3 to 98% halfway through their training before the team updated their device with a new algorithm.

The improvement observed in participants 1 and 3 is related to the improvement in feature discrimination, which is the ability of the algorithm to distinguish the pattern of brain activity encoding for “going left” thoughts from that for “going right.” The team found that the best feature characterizes not only the result of the machine’s machine learning but also the learning in the participants’ brains. The EEG of participants 1 and 3 showed clear shifts in brain wave patterns as they improved accuracy in the machine’s mind control.

“We see from the EEG results that the subject has enhanced the skill of modulating different parts of their brains to generate a pattern for ‘go left’ and a different pattern for ‘go right’,” Millan says. “We think there was a cortical reorganization that occurred as a result of the participants’ learning process .”

Compared to participants 1 and 3, participant 2 had no significant changes in brain activity patterns throughout the training. Its accuracy increased only slightly during the first few sessions, which remained stable for the rest of the training period. Millan says he points out that machine learning alone is insufficient to successfully maneuver such a mind-controlled device

By the end of the training, all participants were asked to drive their wheelchairs through the crowded hospital room. They had to navigate obstacles such as a room divider and hospital beds, which were set up to simulate a real-world environment. Both Participants 1 and 3 completed the task while Participant 2 failed to complete it.

“It appears that for someone to gain such good control over the brain-machine interface that allows them to perform a relatively complex daily activity such as driving a wheelchair in a natural environment, it requires some neuroplastic reorganization in our cortex,” Millan says.

The study also emphasized the role of long-term training for users. Although Participant 1 performed exceptionally well in the end, he struggled in the first few training sessions as well, Millan says. The longitudinal study is one of the first to evaluate the clinical translation of a non-invasive brain-machine interface technique in quadriplegic subjects.

Next, the team wants to know why Participant 2 did not experience the learning effect. They hope to conduct a more detailed analysis of all participants’ brain signals to understand differences and possible interferences for people experiencing learning in the future.

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