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 826–850 of 1655 papers

TitleStatusHype
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction—0
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification—0
Characterising Alzheimer's Disease with EEG-based Energy Landscape AnalysisCode0
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization—0
Analysis of EEG data using complex geometric structurization—0
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing—0
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks—0
Classification of multivariate weakly-labelled time-series with attentionCode0
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals—0
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder—0
Geometric feature performance under downsampling for EEG classification tasks—0
EEGs disclose significant brain activity correlated with synaptic fickleness—0
Mind the beat: detecting audio onsets from EEG recordings of music listening—0
HNPE: Leveraging Global Parameters for Neural Posterior EstimationCode1
End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax—0
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning—0
A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure predictionCode0
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020Code0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM ModelCode1
ScalingNet: extracting features from raw EEG data for emotion recognition—0
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition—0
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG ClassificationCode1
Few-shot time series segmentation using prototype-defined infinite hidden Markov models—0
Coherence of Working Memory Study Between Deep Neural Network and Neurophysiology—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