Researchers use machine learning to pull interest signals from readers' brain waves

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.