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Exactly why people dream is still unknown, but recent research may bring us closer to understanding dreams with the use of computer technology.

New research suggests a computer can predict what you dream based on brainwave activity. By monitoring a person's brain activity during the day when they are awake, researchers were able to distinguish certain dream images while the dreamer was asleep.

"We know almost nothing about the function of dreaming," said study co-author Masako Tamaki, a neuroscientist at Brown University. "Using this method, we might be able to know more about the function of dreaming."

The research may also help dream scientists understand the brain activity behind nightmares. The findings were published Thursday in the journal Science.

There have been many theories on why people dream, including the popular works of Sigmund Freud who believed dreams had to do with wish fulfillment. Freud thoroughly analyzed dream contents as symbols which were relevant in waking life. Some believed dreams to merely be an irrelevant process during the sleep cycle. Other theories suggest dreams allow the brain to continue analyzing daily activities.

Researchers used functional magnetic resonance imaging (fMRI) on three participants while they were asleep. The participants were awoken every few minutes to describe their dreams, providing researchers with about 200 visual images upon completion of the study.

Tamaki and her team then matched the participants dream content to the description in their waking moments and correlated them to specific patterns in brain activity. This was based on the blood flow in fMRI scans, which were transferred as signatures into a computer model.

Once the computer model understood the signatures of the fMRI scan, it analyzed each participant's dreams. The model was to distinguish specific objects in the dreams based on the participants' brain activity when they were awake.

While the team just tried to read dream imagery from one person's waking brain activity, some common patterns for broad classes of imagery were found, such as scenery versus people, according to Live Science.

"There is a similarity amongst the subjects, so from that result, we could pick up some basic dream content and then we can build a model from those base contents, and they may apply to other people," Tamaki said.