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 726–750 of 1655 papers

TitleStatusHype
Sleep Staging Based on Multi Scale Dual Attention Network—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion RecognitionCode1
Motor Imagery Classification based on CNN-GRU Network with Spatio-Temporal Feature Representation—0
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder—0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfacesCode0
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
Sleep syndromes onset detection based on automatic sleep staging algorithmCode0
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
Complex network modelling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis—0
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks—0
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals—0
Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification—0
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based ModelCode1
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals—0
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices—0
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingCode1
Artifact Detection and Correction in EEG data: A Review—0
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing SignalsCode0
Neuroadaptive electroencephalography: a proof-of-principle study in infantsCode0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks—0
A highly scalable repository of waveform and vital signs data from bedside monitoring devices—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