Learning from mistakes: Scientists identify error signals and help to improve movement detection for brain-machine interfaces
Click to enlarge. See image caption below.
In a study published in the journal PLoS ONE, Tomislav Milekovic and colleagues from the Bernstein Center at the University of Freiburg and Imperial College London demonstrate that such errors can in fact be detected from human brain signals. As a source for the data in which they looked for error signals, they used a method called electrocorticography (ECoG). This procedure measured changes in electrical potential at the surface of the brain. Because electrodes are not implanted into human skulls merely for the purpose of a scientific study, the researchers depended on the help from patients who had such electrodes implanted for medical reasons. When the subjects carried out a continuous movement, the computer programme detected errors with high precision within less than half a second. Even when the researchers focused on the data output of only 4 electrodes covering a small square of brain surface, they were able to extract from these locations 82% of detection information for outcome error and 74% of detection information for execution error.
In future, the error detection method presented by the team from London and Freiburg could correct errors that occur during BMI operation, and furthermore to adapt a BMI algorithm to make fewer errors. The results also show that even very small implants are sufficient to detect these errors. As smaller implants mean a reduced medical risk during implantation, their findings may help mass-produced brain-machine interfaces to become a reality. In Freiburg, it is the newly founded Cluster of Excellence BrainLinks-BrainTools that pursues this goal.
Original publication (open access)
Milekovic T, Ball T, Schulze-Bonhage A, Aertsen A, Mehring C (2013) Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements. PLoS ONE 8(2): e55235. doi:10.1371/journal.pone.0055235
Image caption:
Application of neural activity based error detection for improvement of a continuous BMI control. If a BMI decodes an intended movement correctly, no neuronal error signal is elicited. If a so-called execution error is detected, the algorithm can be adapted to reduce the number of errors in decoding in the future. If the unwanted movement causes the cursor to reach an unwanted target, an outcome error signal may be evoked. If this is detected by the BMI system, it can change the decoding algorithm as well, this time in a different way.