By designing a small and efficie brain-computer ierface (BMI), researchers have taken a big step towards improving the quality of life of people with severe motor disabilities. This new technology is able to convert brain signals io text, thus allowing people with moveme disorders to communicate effectively with others.
According to Tekna technology media medical and health news service, the microcomputer brain ierface (MiBMI), which was developed by researchers at the Federal Polytechnic University of Lausanne (EPFL), is based on silicon chips and has the ability to decode complex neural signals and convert them io understandable text. has to read MiBMI allows us to convert complex neural activity io comprehensible text with high accuracy and low energy consumption, says Mahsa Shahifan, one of the researchers of this project. This advance brings us closer to practical, implaable solutions that can significaly improve the communication abilities of people with severe motor disabilities.
In rece years, research in the field of brain-computer ierfaces has made significa progress. These small devices have the poteial to help people with severe mobility disabilities ieract with the world around them and participate in everyday activities. Neuralink company under the guidance of Elon Musk is also active in the developme of brain and computer ierfaces. The company aims to create ierfaces that can help people with neurological diseases such as paralysis or spinal cord injury. Compared to traditional brain-computer ierfaces, the new MiBMI is more compact, efficie and flexible. Traditional systems are often large, consuming and have application limitations. To convert brain signals io text, this device decodes the neural signals produced when a person imagines writing. Electrodes implaed in the brain record the neural activity associated with these imaginary hand movemes. The MiBMI chip then processes these signals and converts them io digital text.
This new technology can help people with locked-in syndrome and other severe motor disabilities to communicate effectively. Mohammad Ali Shaari, the main author of this research, says that while this chip has not yet been iegrated io an operational brain-computer ierface, the results of the experimes show that this device can convert handwritten activity io text with high accuracy. Considering the curre ability of this chip to decode 31 characters, there is a great poteial for future developmes. Researchers hope to expand the use of this technology by increasing the number of decipherable characters.
The small size, low power consumption and low invasiveness of this chip make it suitable for implaation in the brain. The iegrated design of this device enables the recording and processing of neural signals in a small package. This developme could revolutionize the treatme of neurological diseases and help people with severe motor disabilities live a better quality of life.
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