New breakthrough in the development of more natural and continuous BCI control systems


For the first time, the intention of continuous movement could be read from non-invasive brain signals at TU Graz. This success allows for a more natural and non-invasive control of neuroprostheses in real time.

Intended to give paraplegic people a certain freedom of movement and therefore a better quality of life, brain-computer interfaces (BCI) measure the person’s brain activity and convert electrical currents into control signals for neuroprostheses. “Control by thought”, as Gernot Müller-Putz puts it in simplified terms. The director of the Institute of Neural Engineering at the University of Technology Graz (TU Graz) is an “old man” of BCI research and is extensively involved in non-invasive BCI systems.

He and his team have had initial positive results with EEG testing of neuroprostheses or robotic arms in people with spinal cord injuries over the past decade. However, until now, the control was unnatural and cumbersome as the thought patterns had to be imagined over and over. As part of their recently completed ERC Consolidator Grant “Feel your Reach” project, Müller-Putz and his team have now made a breakthrough in the development of more natural and continuous BCI control systems.

It all comes down to seeing

Researchers at TU Graz have for the first time succeeded in controlling a robotic arm solely through real-time thought in the usual non-invasive manner using an EEG cap. This was made possible by decoding the intention of continuous movement from brain signals – something previously impossible. The researchers first looked at a variety of motion parameters such as position, speed, and distance, and extracted their correlates of neuronal activity.

The contribution of the eyes is essential here. It is important that users are allowed to use their eyes to follow the path of the robotic arm. “

Gernot Müller-Putz, Director, Institute for Neural Engineering, Graz University of Technology

However, eye movements and blinking generate their own electrical signals, called eye artifacts in the EEG. “These artifacts distort the EEG signal. They therefore need to be removed in real time. However, it is essential that hand-eye coordination can take place and thus help decode motion requests,” explains Müller-Putz. In other words, visual information helps capture the intention to move. Unwanted signals from the eye itself, however, must be filtered out from electrical activity arithmetically.

BCI detects unwanted movements

It is also essential that one of the BCIs developed by the researchers is able to recognize if a person wants to start a movement – it can recognize the start of a goal-oriented movement. In addition, another BCI from the research team detects and corrects errors, i.e. unwanted movements of the robotic arm; one more piece of the puzzle for more natural prosthetic control. “The brain’s error response can be read from the EEG. The BCI recognizes that the movement performed does not correspond to the person’s intention. It stops the movement of the robotic arm or resets it to the start,” explains Müller-Putz. In the project, error detection has been tested with success several times in tests with people with spinal cord injuries.

People can feel the movements of the robotic arm

Researchers at TU Graz have also been successful with so-called kinesthetic feedback. “The participants not only see the movements of the prosthesis, they also feel them,” says Müller-Putz, visibly delighted. Technically, this was made possible by vibration sensors. These are glued to the skin of the scapula and follow the movements of the robotic arm in finely fluid vibrations. Theoretically, it is also possible that completely paralyzed people feel movement. “However, here we have to consider an application in the neck region”, explains Müller-Putz, alluding to future goals. Above all, the researchers want to improve the decoding of a movement from visual, intentional and kinesthetic information, thereby detecting errors and uniting the four BCI systems in a “quadruple BCI system”.


About Author

Comments are closed.