Brain-computer interfaces: AI reduces need for recalibration
Brain-computer interfaces (BCI) are devices that enable individuals with motor disabilities such as paralysis to control prosthetic limbs, computer cursors, and other interfaces using only their minds. One of the biggest problems facing BCI used in a clinical setting is instability in the neural recordings themselves. Over time, the signals picked up by BCI can vary, and a result of this variation is that an individual can lose the ability to control their BCI.
As a result of this loss of control, researchers ask the user to go through a recalibration session which requires them to stop what they’re doing and reset the connection between their mental commands and the tasks being performed. Typically, another human technician is involved just to get the system to work.
“Imagine if every time we wanted to use our cell phone, to get it to work correctly, we had to somehow calibrate the screen so it knew what part of the screen we were pointing at,” says William Bishop, who was previously a Ph.D. student and postdoctoral fellow in the Department of Machine Learning at CMU and is now a fellow at Janelia Farm Research Campus. “The current state of the art in BCI technology is sort of like that. Just to get these BCI devices to work, users have to do this frequent recalibration. So that’s extremely inconvenient for the users, as well as the technicians maintaining the devices.”
The paper, “A stabilized brain-computer interface based on neural manifold alignment,” presents a machine learning algorithm that accounts for these varying signals and allows the individual to continue controlling the BCI in the presence of these instabilities. By leveraging the finding that neural population activity resides in a low-dimensional “neural manifold,” the researchers can stabilize neural activity to maintain good BCI performance in the presence of recording instabilities.
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