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Found 10 papers, 3 papers with code
Deep learning applied to EEG data with different montages using spatial attention
Here, we explore using spatial attention applied to EEG electrode coordinates to perform channel harmonization of raw EEG data, allowing us to train deep learning on EEG data using different montages.
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.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
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.
A streamable large-scale clinical EEG dataset for Deep Learning
Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research.
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.
