Non-invasive BCI to control robotic arm developed

Researchers at Carnegie Mellon University and the University of Minnesota have developed a non-invasive brain-computer interface (BCI) to control a robotic arm by using novel sensing techniques combined with machine learning to improve the neural decoding of electroencephalogram signals. These techniques enabled the real-time continuous control of a robotic arm in two dimensions, smoothly following a cursor around a screen.
Bin He, head of Carnegie Mellon’s Biomedical Engineering Department, said: “This work represents an important step in non-invasive brain-computer interfaces, a technology that someday may become a pervasive assistive technology aiding everyone, like smartphones. Despite technical challenges using non-invasive signals, we are fully committed to bringing this safe and economic technology to people who can benefit from it. There have been major advances in mind-controlled robotic devices using brain implants. It’s excellent science, but non-invasive is the ultimate goal. Advances in neural decoding and the practical utility of non-invasive robotic arm control will have major implications on the eventual development of non-invasive neurorobotics.”