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 451–475 of 1655 papers

TitleStatusHype
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method—0
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment—0
Can Musical Emotion Be Quantified With Neural Jitter Or Shimmer? A Novel EEG Based Study With Hindustani Classical Music—0
A novel spike-and-wave automatic detection in EEG signals—0
Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks—0
DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition—0
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder—0
DASPS: A Database for Anxious States based on a Psychological Stimulation—0
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models—0
Calibration of off-the-shelf low-cost wearable EEG headset for application in field studies—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
Cross-Subject Data Splitting for Brain-to-Text Decoding—0
Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach—0
Analysis of Relation between Motor Activity and Imaginary EEG Records—0
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning—0
Decline of long-range temporal correlations in the human brain during sustained wakefulness—0
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs—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