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
Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback—0
Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA—0
Modified swarm-based metaheuristics enhance Gradient Descent initialization performance: Application for EEG spatial filtering—0
End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax—0
Classification of Epileptic EEG Signals by Wavelet based CFC—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier—0
Energy-Efficient Tree-Based EEG Artifact Detection—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Benchmarking Domain Generalization on EEG-based Emotion Recognition—0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
Classification of grasping tasks based on EEG-EMG coherence—0
Epileptic Seizure Forecasting: Probabilistic seizure-risk assessment and data-fusion—0
Detecting Dementia from Speech and Transcripts using Transformers—0
Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Classification of Imagined Speech Using Siamese Neural Network—0
Ensemble approach for detection of depression using EEG features—0
Ensemble Classifier for Eye State Classification using EEG Signals—0
Ensemble emotion recognizing with multiple modal physiological signals—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Ensemble plasticity and network adaptability in SNNs—0
Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals—0
Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience—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