Abstract: Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain–computer interface (BCI) systems. However, existing methods often face a critical tradeoff ...

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 ...

If you're a regular PCGamesN reader, you'll know by now that I'm obsessed with Arc Raiders. I know that's hardly a maverick, edgy take to have right now, but it's true. I absolutely love it. However, ...

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

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 ...

Introduction: Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI ...

Abstract: Electroencephalography (EEG)-based seizure prediction has garnered significant attention in epilepsy management, with deep learning methods enhancing prediction performance. However, ...

If there's one thing you want in a PvP game, it's parity. Fairness. Justice. Ok, maybe justice only rears its head when Batman logs onto Fortnite in his favorite Ariana Grande skin, but generally you ...