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 526–550 of 1655 papers

TitleStatusHype
A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)—0
A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory—0
A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning—0
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning—0
Brain Model State Space Reconstruction Using an LSTM Neural Network—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
Brain informed transfer learning for categorizing construction hazards—0
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification—0
An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm—0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials—0
An Investigation on Non-Invasive Brain-Computer Interfaces: Emotiv Epoc+ Neuroheadset and Its Effectiveness—0
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques—0
Adaptive Template Enhancement for Improved Person Recognition using Small Datasets—0
A channel attention based MLP-Mixer network for motor imagery decoding with EEG—0
Brain-based control of car infotainment—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
Brain Age from the Electroencephalogram of Sleep—0
Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention—0
An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation—0
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training—0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—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