Neurological Prediction through EEG: This system utilizes cutting-edge AI, including a combination of Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), to interpret EEG data ...
Abstract: Most EEG-based biometrics rely on either convolutional neural networks (CNNs) or graph convolutional networks (GCNNs) for personal authentication, potentially overlooking the limitations of ...
Medical Xpress on MSN18d
Advancements in reinforcement learning for personalized patient careDr. Logan Grosenick, assistant professor of neuroscience in psychiatry Adapting convolutional neural networks to interpret graph ... framework that generalizes CNNs to graphs. "We're now using it for ...
Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these ...
AZoRobotics on MSN17d
Reinforcement Learning for Personalized Treatment PlansA study reveals reinforcement learning's potential in healthcare for treatment planning, emphasizing the need for improved ...
This paper presents SL-GCNN, a novel Graph Convolutional Neural Network framework specifically designed for granular skeletal motion recognition. SL-GCNN incorporates Space-Time Feature Harmonization ...
introduced an innovative epileptic seizure classification model based on Iterative Gated Graph Convolutional Networks (IGGCN). This model effectively captures long-term dependencies in EEG data ...
Adapting Convolutional Neural Networks to Interpret Graph Data In a second NeurIPS publication presented ... "We're now using it for modeling EEG (electrical brain activity) data in patients. We can ...
This repository provides a demonstration of a deep learning-based system for detecting melanoma from grayscale images. The model predicts whether an input image is classified as "BENIGN" or "MELANOMA, ...
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