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 101–150 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
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEGCode1
EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural NetworkCode1
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
Real-Time Emotion Classification Using EEG Data Stream in E-Learning ContextsCode1
HNPE: Leveraging Global Parameters for Neural Posterior EstimationCode1
Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM ModelCode1
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG ClassificationCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleepCode1
Correct block-design experiments mitigate temporal correlation bias in EEG classificationCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural NetworkCode1
Real-time noise cancellation with Deep LearningCode1
Disguising Personal Identity Information in EEG SignalsCode1
Deep Multi-Task Learning for SSVEP Detection and Visual Response MappingCode1
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task LassoCode1
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoisingCode1
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEGCode1
EEG Channel Interpolation Using Deep Encoder-decoder NetwoksCode1
Automatic detection of microsleep episodes with deep learningCode1
Using Deep Networks for Scientific Discovery in Physiological SignalsCode1
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceCode1
Uncovering the structure of clinical EEG signals with self-supervised learningCode1
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysisCode1
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete PipelineCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level EstimationCode1
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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