Electroencephalogram (EEG)
332 papers with code • 3 benchmarks • 7 datasets
Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
Libraries
Use these libraries to find Electroencephalogram (EEG) models and implementations
SEED
ICLabel
CWL EEG/fMRI Dataset
Subtasks
Latest papers
Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural Networks
mike-boop/match-mismatch-decoders-ojsp-2023
•
•
The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception.
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Finally, a Decoder layer is employed to reconstruct the artifact-reduced EEG signal.
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
ispamm/mhyeeg
•
•
Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information.
Artificial Intelligence for EEG Prediction: Applied Chaos Theory
Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net
•
•
In the present research, we delve into the intricate realm of electroencephalogram (EEG) data analysis, focusing on sequence-to-sequence prediction of data across 32 EEG channels.
mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection
This work introduces a new multispectral database and novel approaches for eyeblink detection in RGB and Near-Infrared (NIR) individual images.
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
In the domain of machine learning algorithms, the significance of the loss function is paramount, especially in supervised learning tasks.
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
A natural method is to learn the temporal dynamic patterns.
Decoding Natural Images from EEG for Object Recognition
dongyangli-del/eeg_image_decode
•
•
This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition.
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior.
ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
ruiqirichard/eegeyenet-vit
•
•
In this study, we demonstrate the application of a hybrid Vision Transformer (ViT) model, pretrained on ImageNet, on an electroencephalogram (EEG) regression task.
