Abstract
Since over a hundred years the electroencephalography (EEG) has been a neurophysiological tool to
observe neuronal activity at the timescale of milliseconds. From early brainwave recordings to
today’s high‐density EEG systems, this technology provides a unique window into the dynamics of
the human brain.

Recent breakthroughs in deep learning now open entirely new possibilities: by automatically
analyzing complex EEG patterns, neural networks can detect subtle changes in brain activity—far
beyond the capabilities of traditional methods. This enables more accurate classification of mental
disorders and the development of individualized prediction models for disease progression and
treatment response.

This presentation offers an overview of the history of EEG research, current deep learning
applications, and future perspectives for a data‐driven and objective psychiatry.