Robotic instruments have been assisting clinical staff and engineers alike for many years, with prosthetic robotic arms, which allow people who have lost a limb to regain freedom of movement, currently in development.

Brain-computer interfaces (BCI), a collaboration between the brain and a device, enable signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb has garnered much interest in robotic prosthetics.  However, to-date BCIs successful in controlling robotic arms have needed highly invasive brain implants and surgeries.

Now, a study led by researchers at Carnegie Mellon University develops a non-invasive, mind-controlled robotic arm capable of continuously tracking and following a computer cursor. The team states being able to non-invasively control robotic devices using only thoughts will have broad applications, in particular benefiting the lives of paralyzed patients and those with movement disorders. The opensource study is published in the journal Science Robotics.

Non-invasive human-machines

Previous studies show a grand challenge in BCI research is to develop less invasive or even totally non-invasive technology enabling paralyzed patients to control their environment or robotic limbs using only their own thoughts. Such non-invasive BCI technology would bring such much-needed technology to numerous patients and the general population.

However, BCIs adopting non-invasive external sensing, rather than brain implants have led to less precise control in the past. The current study uses novel sensing and machine learning techniques to access signals deep within the brain, achieving a high resolution of control, non-invasively over a robotic arm.

The current study utilizes non-invasive neuroimaging and a novel continuous pursuit paradigm to overcome EEG signals leading to significantly improved EEG-based neural decoding and facilitating real-time continuous robotic device control. The technology was tested in 68 able-bodied patients, using up to 10 sessions for each subject, including virtual device control and controlling of a robotic arm for the continuous pursuit of a cursor on a computer screen.

Continuous tracking achieved

Results show the non-invasive BCI successfully decoded neural signals, allowing participants to control a robotic arm in real-time, instructing it to continuously and smoothly follow the movements of a cursor on a screen. Data findings show the new technique enhances BCI learning by nearly 60% for traditional center-out tasks and enhances continuous tracking of a computer cursor by over 500%.

The team states their new framework addresses and improves BCI by increasing user engagement and training, as well as the spatial resolution of non-invasive neural data through EEG source imaging.

The team surmise they have developed a successful, non-invasively
controlled, high-resolution robotic device using only thoughts. For the future, the researcher state their technology is directly applicable to patients and now plans to conduct clinical trials.

Source: Carnegie Mellon University

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