Abstract: In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on ...

(A) Overall structure of the model. MLP, multilayer perceptron. (B) Structure of the time encoder module. (C) Structure of the channel encoder module. BN, batch normalization. “Domain bias caused by ...

There was an error while loading. Please reload this page.

AI powered analysis of routine EEG scans is now distinguishing Alzheimer’s disease from frontotemporal dementia while also estimating disease severity, offering faster and more affordable pathways to ...

Dr Andrei Alexandrov discusses his experience implementing point-of-care EEG equipped with artificial intelligence. As neurologists, our responsibility goes beyond interpreting electroencephalograms ...

In this study, researchers developed a deep learning framework to analyse EEG signals from individuals with Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls. The model ...

Summary: New research shows that deep learning can use EEG signals to distinguish Alzheimer’s disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of ...

Abstract: Electroencephalography is a clinical technique which reads the scalp electrical activity from brain structures. The electroencephalogram (EEG) records the scalp surface using metal ...