EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods ...

To figure out if there’s a grain of truth to all these anecdotes, a team of German scientists at the Hamburg University, led ...

Psychogenic non-epileptic seizures (PNES, pseudoseizures) are common and occur in a wide variety of populations. A proportion of patients have antecedent epilepsy (10–15%) or learning disability ...

When Jocelyn Leitzinger had her university students write about times in their lives they had witnessed discrimination, she noticed that a woman named Sally was the victim in many of ...

EEG biosensors are non-invasive devices that detect and record ... Their growing application scope, coupled with advancements in wireless technology and signal processing algorithms, is reshaping how ...

Analog and mixed signal content is adding risk to ASIC designs. Pessimists see the problem getting worse, while optimists point to AI and chiplets for relief.

These innovations enable real-time brain signal processing, overcoming delays associated with traditional methods. 2 Method The present study employs the Linearly Constrained Minimum Variance (LCMV) ...

A team of researchers from the University of Maryland, the University of Glasgow and Nokia Bell Labs, Cambridge, is working to change that—starting with something millions of people already own: ...

A theater-based immersive neuroaesthetics research program led by Tsinghua University that synchronously captures the neural ...

The UTS team is using it to read his thoughts. A pioneering AI model, developed by Dr Leong, PhD student Charles (Jinzhao) Zhou and his supervisor Chin-Teng Lin, uses deep learning to translate the ...

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system.