New Brain-Computer Interface Transforms Thoughts to Images

TheDigitalArtist/Pixabay

Source: TheDigitalArtist/Pixabay

Achieving the next level of brain-computer interface (BCI) advancement, researchers at the University of Helsinki used artificial intelligence (AI) to create a system that uses signals from the brain to generate novel images of what the user is thinking and published the results earlier this month in Scientific Reports.

“To the best of our knowledge, this is the first study to use neural activity to adapt a generative computer model and produce new information matching a human operator’s intention,” wrote the Finnish team of researchers.

The brain-computer interface industry holds the promise of innovating future neuroprosthetic medical and health care treatments. Examples of BCI companies led by pioneering entrepreneurs include Bryan Johnson’s Kernel and Elon Musk’s Neuralink.  

Studies to date on brain-computer interfaces have demonstrated the ability to execute mostly limited, pre-established actions such as two-dimensional cursor movement on a computer screen or typing a specific letter of the alphabet. The typical solution uses a computer system to interpret brain-signals linked with stimuli to model mental states. Seeking to create a more flexible, adaptable system, the researchers created an artificial system that can imagine and output what a person is visualizing based on brain signals. The researchers report that their neuroadaptive generative modeling approach is “a new paradigm that may strongly impact experimental psychology and cognitive neuroscience.”

The University of Helsinki researchers used a combination of a generative neural network with neuroadaptive brain interfacing to create a new BCI paradigm. Neuroadaptive generative modeling is the estimation of a person’s intentions via adapting a generative model to neural activity. To expand capabilities and not be limited to pre-defined categories, the researchers based the solution on a generative adversarial network (GAN) to generate novel information from a latent representation of an input space.

Generative adversarial networks are a relatively recent