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 ...

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 ...

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 ...

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, ...

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 ...

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 ...

Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Introduction: This scientific investigation explored how meditation ...

This project demonstrates the design and development of an open-source, homebrew single-lead EEG acquisition and preprocessing system. It spans circuit-level prototyping, simulation (Simscape), ...

Abstract: This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer ...