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Found 11 papers, 3 papers with code
Hierarchical Event Descriptor library schema for EEG data annotation
We demonstrate the use of the HED-SCORE library schema to annotate events in example EEG data stored in Brain Imaging Data Structure (BIDS) format.
An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA
A multitude of ICA algorithms for EEG decomposition exist, and in the past, their relative effectiveness has been studied.
A Comparison of Neuroelectrophysiology Databases
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration.
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithm
Independent component analysis (ICA), is a blind source separation method that is becoming increasingly used to separate brain and non-brain related activities in electroencephalographic (EEG) and other electrophysiological recordings.
NEMAR: An open access data, tools, and compute resource operating on NeuroElectroMagnetic data
To take advantage of recent and ongoing advances in large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data.
Assessing learned features of Deep Learning applied to EEG
We applied these methods to a high-performing Deep Learning model with state-of-the-art performance for an EEG sex classification task, and show that the model features a difference in the theta frequency band.
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features
Interestingly we show that the neural network tailored to process EEG spectral features has increased performance when applied to raw data classification.
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution.
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable.
