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 ...

Abstract: Alzheimer's disease (AD), is a prevalent neurodegenerative disorder, characterized by cognitive decline. Alongside AD, and Frontotemporal dementia (FTD) poses significant challenges in ...

@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, ...

Selective attention involves prioritising relevant sensory input while suppressing irrelevant stimuli. It has been proposed that oscillatory alpha-band activity (~10 Hz) aids this process by ...

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 ...

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved ...

An inspector general report to be released on Thursday examined the defense secretary’s use of a private messaging app to discuss airstrikes in Yemen. By Robert Jimison Megan Mineiro and John Ismay ...

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, ...