By measuring brain signals and implementing a feedback scheme, researchers from India and the UK have reduced the positional error in brain-controlled robot arms by a factor of 10, they report, in IEEE/CAA Journal of Automatica Sinica.
This could pave the way to greatly enhancing the quality of life for people suffering from strokes and neuromuscular disorders, according to a media release from Chinese Association of Automation.
Brain-computer interfaces (BCI) have seen a large influx of research in an effort to allow precise and accurate control of physical systems, such as position control of robotic arms, using only signals generated from the user’s brain. Existing brain-computer interfaces, however, are hindered by two major challenges.
First, most of the existing routines for BCI utilize open-loop control. In other words, the routines do not incorporate any feedback during the brain signal-driven movement to correct for any errors. This results in the failure of the system to take corrective actions and leads to large positional errors, such as a robot arm overshooting the desired position and pose. Second, contemporary BCI are designed to respond to inputs sequentially without finer adjustments, leading to further errors in positional control. Additionally, many BCI utilize multiple sensors to drive the functionality of the device under control. Sensors such as infrared spectroscopy, electroencephalography (EEG), and functional magnetic resonance imaging may be used in combination to process signals from the brain.
EEG to Control Robotic Arm
In their study, researchers relied solely on EEG, due to its non-invasiveness, fast response time, and low cost. By using sophisticated processing techniques, the researchers were able to partition different brain signals from the EEG necessary to control a robot arm. The team then made use of a well-known brain signal, the P300, which appears when a subject notices a significant but rare stimulus. In this case, when the subject notices the robotic arm does not reach the position they originally desired.
“The P300 is used to freeze the current motion of the robotic arm.
“Since elicitation and detection of the P300 signal requires a finite amount of time, the robotic link crosses the target position by a small amount before motion is stopped. The link is then moved in the reverse direction of the last motion before it is stopped.”
— Amit Konar, Professor in the Department of Electronics and Tele-Communication Engineering, Jadavpur University and co-author of the study
Each subsequent stoppage and reversal of the robotic link reduces the speed at which the arm is moving, until a minimum speed is met and the movement ceases. By introducing P300 brain responses to the arm movement via a feedback mechanism, the team was able to bring the error of arm movements down from 2.1% to 0.20% when compared to the previous state of the art BCI.
The team plans to build on their BCI design by developing a more robust, noise-insensitive control interface, per the release.
[Source(s): Chinese Association of Automation, EurekAlert]
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