Every thought leaves a trace — a faint electrical blip on the scalp. A device with dozens of wires, resembling a swim cap, catches these traces and turns them into wavy lines. From those lines, you can guess what a person feels even if they stay silent. In a new study, three computer programs competed to see which could read such lines most accurately and determine whether the mood was bad, neutral, or good.

The first program, logistic regression, works like a meticulous secretary: it sorts signals into neat categories and looks for clear boundaries. The second, support vector machines, acts like a strict border guard, drawing a line between classes with the widest possible margin. The third, random forest, gathers hundreds of small decisions into one collective verdict, like a flock of sparrows where each bird views the task from its own branch, yet together they find food without fail.

The trials showed that the random forest recognized emotions most accurately. Its accuracy and F1 score (the harmonic mean of precision and recall) were higher than those of the other two programs, and even surpassed previous best models. This means the forest was better at capturing how the brain 'sounds' during joy, anxiety, or indifference.

This approach could be useful in smart devices that adapt to mood, from games that change their storyline to assistants that sense driver fatigue. But before widespread use, more data is needed — the sample was limited so far, and the forest must prove its strength on new people.