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 1301–1325 of 1655 papers

TitleStatusHype
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes—0
A contrastive-learning approach for auditory attention detection—0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games—0
Adaptive neural network classifier for decoding MEG signals—0
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring—0
Adaptive Template Enhancement for Improved Person Recognition using Small Datasets—0
A Data Driven Approach for Resting-state EEG signal Classification of Schizophrenia with Control Participants using Random Matrix Theory—0
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates—0
A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition—0
A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
Advancing Brainwave Modeling with a Codebook-Based Foundation Model—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
Advancing Speech Synthesis using EEG—0
Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces—0
Adversarial Deep Learning in EEG Biometrics—0
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects—0
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Affective Brain-Computer Interfaces: A Tutorial to Choose Performance Measuring Metric—0
Affective EEG-Based Person Identification Using the Deep Learning Approach—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