electroencephalography
PulseAugur coverage of electroencephalography — every cluster mentioning electroencephalography across labs, papers, and developer communities, ranked by signal.
- used by brain–computer interface 90%
- used by Alzheimer's disease 90%
- instance of Meg 90%
- instance of electrocardiography 70%
- instance of brain–computer interface 70%
- used by Erich-Mühsam-Gesellschaft 70%
- instance of Alzheimer's disease 70%
- used by Brain Computer Interfaces 70%
- used by Shap 70%
- used by electrocardiography 70%
- used by Electromyography 70%
- competes with electrocardiography 50%
19 day(s) with sentiment data
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AI framework integrates EEG and video for precise mouse seizure detection
Researchers have developed EEGVFusion, a novel multimodal framework designed to improve seizure detection in mouse models. This system integrates self-supervised EEG learning with spatio-temporal video encoding, utilizi…
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ViBE framework maps visual stimuli to M/EEG brain signals
Researchers have developed ViBE, a new framework for brain encoding that translates visual stimuli into magnetoencephalography (MEG) and electroencephalography (EEG) signals. The system utilizes a spatio-temporal convol…
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AI network improves dementia diagnosis and MMSE prediction using EEG data
Researchers have developed a novel Task-guided Spatiotemporal Network (TGSN) incorporating diffusion augmentation to improve dementia diagnosis and MMSE prediction using EEG data. The TGSN utilizes multi-band feature fu…
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AI framework enhances EEG biomarker generalization for Parkinson's detection
Researchers have developed a new framework to improve the generalizability of EEG biomarkers for detecting Parkinson's disease across different clinical populations. Their approach addresses issues where models trained …
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EEG foundation models benchmarked across architectures and tasks
Researchers have conducted a systematic benchmark of channel adaptation methods for EEG foundation models, evaluating four techniques across five models, five tasks, and two training regimes. The study found that the op…
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New SATTC method improves EEG-to-image retrieval across subjects
Researchers have developed SATTC, a novel method for improving the accuracy of retrieving images based on brainwave (EEG) data. This technique addresses challenges like subject variability and ranking instability in cro…
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New MTEEG framework enables unified multi-task EEG analysis with LoRA
Researchers have developed MTEEG, a novel framework for multi-task electroencephalogram (EEG) analysis. This approach utilizes task-specific low-rank adaptation (LoRA) modules to enable a single pre-trained model to ada…
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BandRouteNet neural network offers adaptive EEG artifact removal
Researchers have developed BandRouteNet, a novel neural network designed to remove artifacts from electroencephalography (EEG) signals. This adaptive, frequency-aware model processes EEG data in specific frequency bands…
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New AI model reconstructs visual cognition from EEG signals with structural guidance
Researchers have developed a Structure-Guided Diffusion Model (SGDM) to reconstruct visual information from electroencephalography (EEG) signals. This new model improves upon existing methods by incorporating explicit s…
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FedSPDnet advances federated learning with geometry-aware aggregation strategies
Researchers have developed FedSPDnet, a novel federated learning framework designed for models that process symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. This framework introduces two a…