Electroencephalogram (EEG)
336 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 with no code
3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention
This model combined MI-EEG signals from different channels into three-dimensional features and extracted spatial features through convolution operations with multiple three-dimensional convolutional kernels of different scales.
Data Contamination Issues in Brain-to-Text Decoding
Decoding non-invasive cognitive signals to natural language has long been the goal of building practical brain-computer interfaces (BCIs).
Study of cognitive component of auditory attention to natural speech events
This study proposes a novel approach to auditory attention decoding by looking at higher-level cognitive responses to natural speech.
Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases
The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold.
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification
Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations.
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses.
EEG-Based Reaction Time Prediction with Fuzzy Common Spatial Patterns and Phase Cohesion using Deep Autoencoder Based Data Fusion
Drowsiness state of a driver is a topic of extensive discussion due to its significant role in causing traffic accidents.
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series.
Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition
Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the common features of multimodal data representing odor information and the individual features in olfactory EEG representing individual emotional information.
HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms
To address these challenges, we propose HypUC, a framework for imbalanced probabilistic regression in medical time series, making several contributions.
