An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...

A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...

EEG-based machine learning predicted SSRI treatment response in depression with high accuracy. Learn how brain signals could ...

Recent advances in the integration of electroencephalography (EEG) with machine learning techniques have provided promising avenues for the early detection and monitoring of Alcohol Use Disorder (AUD) ...

A dual-network hydrogel (PGEH) cross-linked via liquid metal induction was developed exhibiting remarkable mechanical properties and skin-temperature-triggered on-demand adhesion capabilities. The ...

DENVER -- A high seizure burden derived by an artificial intelligence (AI) algorithm was associated with worse outcomes, a retrospective analysis showed. Hospitalized patients who had a high seizure ...

New AI model decodes brain signals captured noninvasively via EEG opens the possibility of developing future neuroprosthetics ...

The first patenting from Encephalogix Inc. details its development of platform that uses machine learning and AI to analyze EEG data that is typically ignored.

Discover how Electroencephalography (EEG) records brain activity to diagnose epilepsy, strokes, and sleep disorders. Learn ...

People with spinal cord injuries often lose movement even though their brains still send the right signals. Researchers tested whether EEG brain scans could capture those signals and reroute them to ...