Japanese neuroscientists have found a way to decode the content of people’s dreams. They presented their findings at the annual meeting of the Society for Neuroscience in New Orleans earlier this month.
The team studied the brains of three participants as they slept. They simultaneously made brain scans and recordings of brain waves using functional neuroimaging and electroencephalography (EEG).
For three-hours, the researchers woke the participants whenever they had started dreaming and asked them each time what they had just dreamed about. Then the participants went back to sleep.
For each participant, the experiment was repeated between seven and ten times on different days, resulting in about 200 dream reports. The verbal reports were used to extract 20 categories that appeared most frequently in the dreams, such as ‘car’, ‘male’, ‘female’, and ‘computer.’
Then the researchers selected photos representing each category and made brain scans while the participants viewed these photos. By comparing these brain activity patterns with those recorded just before the participants were woken up, the researchers were able to identify distinctive patterns in visual brain regions and to built a model to predict whether each category of content was present in the dreams.
“By analysing the brain activity during the nine seconds before we woke the subjects, we could predict whether a man is in the dream or not, for instance, with an accuracy of 75–80%,” says study leader Yukiyasu Kamitani.
“Our study shows that during dreaming, some brain areas show activity patterns similar to those elicited by pictures of related contents,” says Kamitani. “Thus, using a database of picture-elicited brain activity and a pattern recognition algorithm, we can read out, or decode, what a person might be seeing from brain scans during dreaming.”
“In this study we were able to decode only basic object category information, but the method could be extended to decode more dynamic and emotional aspects of dreams.”
Source: Nature, The Telegraph
Photo: mollyjolly/Flickr
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