SOTAVerified

Electroencephalogram (EEG)

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.

Papers

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Showing 1051–1075 of 1655 papers

TitleStatusHype
The Little Prince in 26 Languages: Towards a Multilingual Neuro-Cognitive Corpus—0
Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing—0
Wireless User-Generic Ear EEG—0
Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI dataCode1
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Federated Transfer Learning for EEG Signal ClassificationCode1
Brain-based control of car infotainment—0
Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine InterfacesCode0
Postural control adaptation and habituation during vibratory proprioceptive stimulation: an HD-EEG investigation of cortical recruitment and kinematics—0
Multi-level Binarized LSTM in EEG Classification for Wearable Devices—0
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016—0
Local Model Feature Transformations—0
Deep transfer learning for improving single-EEG arousal detection—0
Object classification from randomized EEG trials—0
Advancing Speech Synthesis using EEG—0
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition—0
Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection—0
TSception: A Deep Learning Framework for Emotion Detection Using EEGCode1
Improving auditory attention decoding performance of linear and non-linear methods using state-space model—0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance—0
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing—0
EEG Signal Classification using Variational Mode Decomposition—0
A Novel Deep Learning Architecture for Decoding Imagined Speech from EEG—0
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All datasetsSEED-IVHS-SSVEPSEED

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy74.35—Unverified
2DGCNNAccuracy69.88—Unverified
3DBNAccuracy66.77—Unverified
#ModelMetricClaimedVerifiedStatus
1MultitaskSSVEPAccuracy (5-fold)92.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DBNAccuracy86.08—Unverified