Drug-resistant epilepsy often requires precise identification of seizure onset zones using SEEG recordings. This article presents a Variational Autoencoder–based deep learning framework that detects and interprets seizure onset phases with transparency, helping clinicians accelerate diagnosis and build trust in AI-assisted neurology.

Explore how a dancer’s brain responds to ballet movements. EEG research uncovers changes in brain activity during jumps, spins, and pliés.

Alzheimer’s disease disrupts brain networks and is marked by shifts in brain rhythms detectable by EEG long before significant cell death. Recognizing these changes is vital for early detection and targeted interventions.

How ChatGPT affects brain activity, memory, and ownership. Learn from a new MIT study on the cognitive impact of AI writing tools and discover smarter usage strategies.

This post presents a new modeling study by our team and collaborators that uncovers the neural mechanisms behind prediction error evaluation and precision weighting in the brain. Using a biophysically grounded Laminar Neural Mass Model (LaNMM), the work offers a plausible implementation of predictive coding principles through cross-frequency coupling across cortical layers.

Can 40-Hz tACS modulate brain connectivity? This EEG study with Starstim 8 and eLORETA investigates frontoparietal dynamics.

As neurotechnology becomes part of everyday reality, the need for global governance grows more urgent. This post explores why a robust international regulatory framework—grounded in neuroethics, human rights, and responsible innovation—is essential to guide the ethical development and deployment of brain technologies.

New research uncovers how an Alzheimer’s EEG biomarker, linked to p-tau181, may reveal early brain changes before symptoms appear.