TinyML Seizure Detector Balances Accuracy and Uncertainty on Edge Devices
A new TinyML framework using Kolmogorov–Arnold Networks enables uncertainty-aware epileptic seizure detection on edge devices, achieving 94.87% accuracy with an ...
A new TinyML framework using Kolmogorov–Arnold Networks enables uncertainty-aware epileptic seizure detection on edge devices, achieving 94.87% accuracy with an ...
A case report documents a rare UBE3A variant in a toddler with developmental delay and abnormal EEG findings suggestive of ...
A 2024 review in BMC Biomedical Engineering details how generative AI techniques are enhancing brain-computer interface accuracy, resolution, and cross-subject ...
An exploratory study of 142 participants found that individuals with food addiction exhibited significant impairments in reward-based decision-making despite minimal ...
A new machine learning framework using reduced-channel EEG and variational mode decomposition achieves 92.16% accuracy in classifying nine types of ...
A study in Brain Informatics shows that EEG can detect when personally familiar faces break through into conscious awareness during ...
A new patient-specific algorithm uses two EEG channels and cepstrum analysis to predict epileptic seizures with high accuracy and low ...
A new deep learning framework called SSA-MTSNet improves EEG-based emotion recognition by jointly modeling spectral, spatial, and temporal dynamics, achieving ...
Researchers developed NVC-DSDNet to align neurovascular delays and disentangle multimodal features in hybrid EEG–fNIRS brain–computer interfaces.
A new study reveals that federated learning for EEG data remains vulnerable to privacy attacks, though differential privacy can significantly ...
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© 2026 Scienmag - Science Magazine