GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Frontiers

osl-ephys: a Python toolbox for the analysis of electrophysiology data

We describe OHBA Software Library for the analysis of electrophysiology data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for ...
IEEE

Speed: Scalable Preprocessing of EEG Data for Self-Supervised Learning

Abstract: Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger models, ...
PNAS

Interoceptive signals shape the earliest markers and neural pathway to awareness at the visual threshold

The brain constantly processes stimuli from inside and outside the body, and interoceptive stimuli can modulate the perception of external stimuli. Cardiac and respiratory rhythms are important ...
Scientific Research Publishing

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study ()

1 Departmentof Electrical and Computer Engineering, Old Dominion University, Norfolk, USA. 2 Sir Salimullah Medical College, Dhaka, Bangladesh. Cardiovascular Diseases (CVDs) pose a significant global ...