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Artificial intelligence is quietly transforming the diagnostic methods in the field of mental health. A research team from Kaunas University of Technology has developed a revolutionary depression diagnosis model that employs multimodal analysis of voice and EEG data, paving the way for precise identification of mental health issues. The core of this research lies in breaking the limitations of traditional single-data diagnostics. The research team chose voice as a key data source because it can subtly reflect emotional states. Speech rate, tone, and emotional energy can all serve as potential signals of depression.

Meta AI has recently launched an advanced AI system capable of generating images from EEG data in an extremely short time. This technology is based on magnetoencephalography (MEG) and holds promise for new possibilities in the development of non-invasive brain-machine interfaces. The system reconstructs the brain's perception and processing of images in almost real-time, aiding in a deeper understanding of the role of images in human intelligence.