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 101–125 of 1655 papers

TitleStatusHype
Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment ClassificationCode1
Embedding Decomposition for Artifacts Removal in EEG SignalsCode1
Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge DevicesCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM modelCode1
IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact RemovalCode1
EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement PredictionCode1
EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer PerceptronCode1
Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder FrameworkCode1
Self-supervised EEG Representation Learning for Automatic Sleep StagingCode1
Low-cost brain computer interface for everyday useCode1
Self-supervised Contrastive Learning for EEG-based Sleep StagingCode1
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activityCode1
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion RecognitionCode1
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based ModelCode1
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingCode1
Classification of Hand-Grasp Movements of Stroke Patients using EEG DataCode1
EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural NetworkCode1
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEGCode1
Robust learning from corrupted EEG with dynamic spatial filteringCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEGCode1
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisCode1
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
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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