Telekinesis: Chinese Scientists Advance Toward Moving Things With Our Thoughts

A published scientific paper entitled “Remotely Mind-controlled Metasurface via Brainwaves” advances the first step towards real-time, remote, and wireless mind control of metamaterials.

When you think of telekinesis, using your mind to move objects at a distance, you think of pure fiction, such as Like Skywalker in Star Wars: Episode V – The Empire Strikes Back or various characters from X-Men. However, it actually is something scientists are working on, and for some of them, the key technology is something called metamaterials.

Metamaterials have attracted extensive attention from many fields due to their extraordinary physical properties. It has provided researchers with a new concept of designing artificial materials, bringing vigor and vitality to advanced functional materials. As the two-dimensional counterpart to metamaterials, metasurfaces have unprecedented freedom in manipulating Electromagnetic (EM) waves.

Through on-site programming, programmable metasurfaces (PMs) with multiple or switchable functions can be realized and further integrated with sensors or driven by pre-defined software. The self-adaptability significantly improves the response rate by removing human involvement. The switches among different functions on these PMs generally rely on manual operation. The fundamental framework is wire-connected, manually-controlled and non-real-time switched. Therefore, it is fascinating to construct an entire framework that can realize remote, wireless, real-time, mind-controlled functional metasurfaces.

Brainwaves Signal Extraction and Transmission Schematic

In the process of brainwave signal extraction and transmission, the TGAM module extracts brainwave signals and converts them to attention value. And the attention information is transmitted remotely from the Bluetooth module to the Arduino, which outputs different voltages by discriminating values. Credit: CAS

In a new paper published in the journal eLight, a joint team of scientists led by Professor Shaobo Qu & Professor Jiafu Wang from Air Force Engineering University and Professor Cheng-Wei Qiu from the National University of Singapore have advanced the first step towards real-time, remote, and wireless mind control of metamaterials. Their paper, titled “Remotely Mind-controlled Metasurface via Brainwaves,” proposes a framework for realizing this goal.

Traditionally, the involvement and participation of humans are usually necessary for many scenarios. A human should control the metasurface with their mind directly. It has also been well established that a human’s brain will generate brainwaves in the process of thinking. The authors theorized that collecting brainwaves and using them as the control signals of metasurfaces would allow the users to control metasurfaces with their minds. It would also improve the response rate of metasurfaces. This development would mark an enormous step towards truly intelligent metasurfaces.

The research team achieved remote control by transmitting brainwaves wirelessly from the user to the controller via Bluetooth. The aim was to utilize the user’s brainwaves to control the EM response of PMs. By taking this route, they demonstrated an RMCM where the user could control the scattering pattern.

The simulated and test results showed that the user’s brainwaves directly controlled the outcome, with a significantly better control rate and switch rate. That indicates that their model was far superior to any existing model or product in the market. Their design can be further customized to improve the accuracy of their equipment.

The research team hopes to combine this with intelligent algorithms and improve the processes in the future. They believe that their work can be readily extended to other mind-controlled functional or multi-functional metasurfaces. It may find applications in areas as diverse as health monitoring, 5G/6G communications, and smart sensors.

Reference: “Remotely mind-controlled metasurface via brainwaves” by Ruichao Zhu, Jiafu Wang, Tianshuo Qiu, Yajuan Han, Xinmin Fu, Yuzhi Shi, Xingsi Liu, Tonghao Liu, Zhongtao Zhang, Zuntian Chu, Cheng-Wei Qiu and Shaobo Qu, 11 June 2022, eLight.
DOI: 10.1186/s43593-022-00016-0