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 76–100 of 1655 papers

TitleStatusHype
From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals—0
Sonic Entanglements with Electromyography: Between Bodies, Signals, and Representations—0
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion—0
Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding—0
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG ModelCode2
Designing Pre-training Datasets from Unlabeled Data for EEG Classification with Transformers—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning—0
Optimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection—0
Geometry-Constrained EEG Channel Selection for Brain-Assisted Speech Enhancement—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
PHemoNet: A Multimodal Network for Physiological SignalsCode2
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data—0
Classification of epileptic seizures in EEG data based on iterative gated graph convolution networkCode0
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG SignalsCode2
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks—0
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective—0
Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings—0
Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep—0
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions—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