electroencephalogram
PulseAugur coverage of electroencephalogram — every cluster mentioning electroencephalogram across labs, papers, and developer communities, ranked by signal.
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New nASR layer enhances real-time EEG artifact removal for BCIs
Researchers have developed nASR, a novel end-to-end trainable neural layer designed to improve the accuracy and speed of artifact subspace reconstruction in electroencephalogram (EEG) signals for real-time brain-compute…
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New framework uses EEG and AR to assess ocular response times for mTBI
Researchers have developed a novel framework that integrates electroencephalogram (EEG) data with augmented reality (AR)-based Vestibular/Ocular Motor Screening (VOMS) tasks to assess ocular response times. This system …
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New BCI Adaptation Method Eliminates Backpropagation for Efficiency
Researchers have developed a novel test-time adaptation (TTA) method called Backpropagation-Free Transformations (BFT) designed for lightweight electroencephalogram (EEG)-based brain-computer interfaces (BCIs). This app…
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Diffusion models generate synthetic EEG data to improve hearing aid attention decoding
Researchers have developed a method using diffusion probabilistic models (DPMs) to generate synthetic electroencephalogram (EEG) data for auditory attention decoding (AAD) in hearing aids. This approach addresses the ch…
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New statistical theory quantifies signature learning rates for path regression
This paper introduces a statistical theory for signature-based path regression, focusing on how quickly finite-level signatures can approximate path-valued data. Researchers established an L^2 approximation rate for smo…
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Deep Learning Models Show Promise for Sleep Apnea Classification via EEG
Researchers have developed deep learning models to classify sleep apnea from electroencephalogram (EEG) signals, aiming to reduce the resource-intensive nature of traditional polysomnography. The study compared various …
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Kolmogorov-Arnold Networks proposed for transparent EEG seizure detection
A new arXiv paper reviews the limitations of traditional deep learning models for electroencephalogram (EEG) seizure detection, highlighting issues with interpretability, data requirements, and computational costs. The …
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Withdrawn paper details GCN-based EEG seizure detection
A research paper, now withdrawn, proposed a framework for detecting epileptic seizures using Graph Convolutional Neural Networks (GCNs) applied to electroencephalogram (EEG) signals. The method involved decomposing EEG …
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DeepTokenEEG model achieves 100% accuracy in Alzheimer's detection
Researchers have developed a new lightweight model called DeepTokenEEG for classifying electroencephalogram (EEG) signals to detect Alzheimer's disease (AD) and mild cognitive impairment. This model utilizes spatial and…