Well organized and synchronized brain wave activity is essential for normal brain functioning. These electrical rhythms ...

Abstract: The timely intervention in Alzheimer disease (AD) requires early detection, which is difficult to achieve in conventional machine and deep learning models because the brain networks have a ...

End-to-end EEG artifact detection pipeline: data preprocessing (Part 1) and CNN training and testing workflow (Part 2). This repository contains the code for the paper "A Lightweight Deep ...

Psychiatry stands at a pivotal turning point shaped by rapid technological advances and pressing clinical demands (1). Mental health disorders, defined by multifaceted etiologies and heterogeneous ...

1. Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of ...

For neural prosthetic devices, accurate classification of high dimensional electroencephalography (EEG) signals is significantly impaired by the existence of redundant and irrelevant features that ...

Researchers at örebro University have developed two new AI models that can analyze the brain's electrical activity and accurately distinguish between healthy individuals and patients with dementia, ...

@article{zini2026alzheimer, title={Alzheimer’s disease classification from EEG using a multiscale temporal deep network}, author={Zini, Simone and Barbera, Thomas and Bianco, Simone and Napoletano, ...