New equipment that can read minds of people and also aids in finding out what song they were listening to has been developed, reveals a new study.
The new study was carried out at D'Or Institute for Research and Education used a Magnetic Resonance (MR) machine to read participants' minds.
The study, published in Scientific Reports, contributes for the improvement of the technique and pave the way to new research on reconstruction of auditory imagination and inner speech.
In the experiment, six volunteers heard 40 pieces of classical music, rock, pop, jazz, and others. The neural fingerprint of each song on participants' brain was captured by the MR machine while a computer was learning to identify the brain patterns elicited by each musical piece.
Musical features such as tonality, dynamics, rhythm and timbre were taken in account by the computer.
After that, researchers expected that the computer would be able to do the opposite way: identify which song participants were listening to, based on their brain activity, a technique known as brain decoding.
When confronted with two options, the computer showed up to 85% accuracy in identifying the correct song, which is a great performance, comparing to previous studies.
Researchers then pushed the test even harder by providing not two but 10 options (e.g. one correct and nine wrong) to the computer. In this scenario, the computer correctly identified the song in 74% of the decisions.
In the future, studies on brain decoding and machine learning will create possibilities of communication regardless any kind of written or spoken language.
"Machines will be able to translate our musical thoughts into songs", says Sebastian Hoefle, researcher from D'Or Institute and PhD student from Federal University of Rio de Janeiro, Brazil.
The study is a result of a collaboration between Brazilian researchers and colleagues from Germany, Finland and India.
According to Hoefle, brain decoding researches provide alternatives to understand neural functioning and interact with it using artificial intelligence.
In the future, he expects to find answers for questions like "what musical features make some people love a song while others don't? Is our brain adapted to prefer a specific kind of music?"