Researchers have created a mind-controlled wheelchair that could potentially improve the lives of individuals with disabilities. For example, in tests, quadriplegics paralyzed from the neck downward managed to move through complex environments solely with their minds.
Scientists develop a wheelchair that quadriplegics can control with their mind
Volunteers changed their orientation by mentally moving particular body parts, such as their hands for a left turn and their feet for a right turn. Neuronal signals were converted into digital motor responses via a computer. Three men donned skullcaps with electrodes attached to them, which used a transmitter to pick up the conversations.
Corresponding study author from the University of Texas at A Jose del R. Milan said that they demonstrated the importance of both brain-machine interface algorithm and user for the successful operation of wheelchairs. He added that the research highlights a possible pathway for enhanced clinical translation for non-invasive brain interface tech.
The wheelchair, as described in the Journal of iScience, will aid quadriplegics in gaining mobility. The individuals involved in the study underwent training sessions thrice a week for 2-5 months.
At first, the patients had the same accuracy levels of almost 43% to 55%, but this increased to 98% over the training course. The headset employs electroencephalography (EEG), involving the attachment of small sensors to the head.
Computer program distinguished patterns for going left or right
A computer program was able to distinguish between patterns encoded for going left and right. The team determined that both machine and people learning led to advancements over time.
Milan added that EEG resulted in the subject consolidating the skill of modulating various parts of the brain to create a ‘go left’ patter and a different ‘go right’ pattern. He added that a cortical reorganization occurred due to the learning process.
By the test’s conclusion, two volunteers could independently manoeuvre their chairs across a crowded hospital room. They navigated around obstacles, including beds and screens that replicated real-world settings. Accuracy improved as the training continued suggesting that machine learning alone wasn’t adequate to manoeuvre a mind-controlled device.
