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
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.
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement
In this work, we therefore propose a novel end-to-end brain-assisted speech enhancement network (BASEN), which incorporates the listeners' EEG signals and adopts a temporal convolutional network together with a convolutional multi-layer cross attention module to fuse EEG-audio features.
Neurophysiological Response Based on Auditory Sense for Brain Modulation Using Monaural Beat
For analysis, we calculated the power spectral density (PSD) of EEG for each session and compared them in frequency, time, and five brain regions.
Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats
The monaural beat is known that it can modulate brain and personal states.
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training.
New Approach for an Affective Computing-Driven Quality of Experience (QoE) Prediction
In human interactions, emotion recognition is crucial.
Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm
The classification accuracy of the proposed method has reached 74. 61% and 81. 19% on datasets 2a and 2b, respectively.
Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling
Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals.
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning.
LGL-BCI: A Lightweight Geometric Learning Framework for Motor Imagery-Based Brain-Computer Interfaces
The efficiency, assessed on two public EEG datasets and two real-world EEG devices, significantly outperforms the state-of-the-art solution in accuracy ($82. 54\%$ versus $62. 22\%$) with fewer parameters (64. 9M compared to 183. 7M).
