MIPT developed new Brain-Computer Interface algorithm in Russia

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Moupiya Dutta
Moupiya Dutta
She finds it interesting to learn and analyze society. she keeps herself updated, emphasizing technology, social media, and science. She loves to pen down her thoughts, interested in music, art, and exploration around the globe.

With technology gaining its intensity, Brain-Computer Interface (BCI) is gaining its popularity due to recent advances in developing small and compact electronic technology and electrodes. Efficiency and form factor reduction, in particular, are the key objectives for Body Sensor Networks (BSNs) and wearable systems that implement BCIs.

Neurorobotics and Moscow Institute of Physics and Technology (MIPT) have recently developed a new brain-computer interface algorithm in Russia. The new technology can use artificial neural networks and EEG displays images in the human brain on a computer screen in real-time.

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Motor imagery can be controlled via the brain-computer interface (BCI) and can stimulate the same neuroplastic, which transforms the EEG signals of the brain appearing during the motor imagery into commands for the external device.

Researchers have made it possible to construct a post-stroke rehabilitation device controlled by brain signals. Though the pictures determined come blurry, yet it can still be distinguished with the general scene categories.

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The brain-computer interface developed by Russian researchers relies on labor a technique that records brain waves through non-invasive electrodes (without surgical implantation). The technology can analyze brain activity and reproduce images seen by humans in real-time.

The brain-computer interface is tested in two phases. In first, healthy people are made to watch randomly selected five 10-second YouTube video clips for a total of 20 minutes. With EEG data, researchers found that the brainwaves of each type of video are different hence helping the research team to analyze the brain’s response to the video in real-time.

In the second phase, researchers randomly select three categories from five categories and developed a native feedback model. Such a technique of feedback model is used to make the brain-computer interface classifier’s prediction results as natural images. Finally, the team then converts the EEG signals into actual images similar to those viewed by the subjects.

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