University of Texas: People’s Minds Can Be Revealed Using a Brain Activity Decoder

When a person listens to a story or imagines telling one, neural signals are generated in the brain that can be measured using an fMRI scanner. With extensive training, AI systems can be taught to translate brain activity into a continuous stream of text, like the sentence “She has not even started to learn to drive yet” when the speaker says, “I don’t have my driver’s license yet.” The accuracy of this translation is remarkable, making it possible to extract stories from an individual’s brain without the need for external cues.

The researchers at The University of Texas created a semantic decoder that is capable of understanding some of the videos that a participant is watching as they undergo brain scans using an fMRI machine. The system accurately described different events from the videos without the need for external cues.

However, this semantic decoder is not practical outside of the laboratory. This is because it requires an extensive amount of time to be put into the training of the AI and a long session to be conducted on a fMRI machine which is not affordable for all and may not be available in every clinical facility.

Although the use of this AI system is still in its early stages, it has great potential and can be further developed in the future. Its ability to reveal a person’s thoughts and decode stories from their minds is an intriguing aspect of AI. If further research is done in the development of this system, it can be used in a variety of applications, including in clinical settings, to reveal stories behind the thinking of individuals and to help mental health professionals.

Read more about AI: