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
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding—0
Hierarchical Trait-State Model for Decoding Dyadic Social Interactions—0
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm—0
High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals—0
Holistic Semi-Supervised Approaches for EEG Representation Learning—0
How Homogenizing the Channel-wise Magnitude Can Enhance EEG Classification Model?—0
Human brain activity for machine attention—0
Human brain distinctiveness based on EEG spectral coherence connectivity—0
Human Brains Can't Detect Fake News: A Neuro-Cognitive Study of Textual Disinformation Susceptibility—0
Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN—0
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction—0
Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition—0
Hybrid Backpropagation Parallel Reservoir Networks—0
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control—0
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition—0
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection—0
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction—0
HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms—0
Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning—0
Identification of Dynamic functional brain network states Through Tensor Decomposition—0
Identification of interictal epileptic networks from dense-EEG—0
Identification of mental fatigue in language comprehension tasks based on EEG and deep learning—0
Identifying anatomical origins of coexisting oscillations in the cortical microcircuit—0
Identifying Attention-Deficit/Hyperactivity Disorder through the electroencephalogram complexity—0
Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead 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