Abstract: Electroencephalogram (EEG) signals to classify sleep stages have emerged as a vital area of research, aiming to provide non-invasive measures of people's neurological and cognitive states.

These connectivity features were identified through a data-driven method, employing machine learning. We carried out some automatic, moderate pre-processing and extracted spectral connectivity ...

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated ...

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

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 ...

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, ...

Acute sleep deprivation significantly impacts cognitive function, contributes to accidents, and increases the risk of chronic illnesses, underscoring the need for reliable and objective diagnosis. Our ...

Start your journey into machine learning with EEG time-series data in this easy-to-follow Python project. Perfect for beginners looking to explore brain signal analysis! #MachineLearning #EEG ...

Abstract: Modern society faces significant challenges related to stress, sadness, and panic. Notably, stress is a major predictor of health disparities linked to socioeconomics. To predict and ...

Design a lightweight machine-learning pipeline that analyzes single-channel frontal EEG data (Fp1/Fp2) and accurately detects driver drowsiness in real-time. 50 Hz IIR notch filter + 0.5–30 Hz ...