This repository implements various deep learning architectures for detecting blinks in EEG signals, comparing performance between healthy subjects and Parkinson's disease patients. Blinks in ...

The source model inversely calculates cortical neural activity by EEG and extracts significantly distinguishable features. A DL network based on multi-granular information (MGIFNet) is introduced to ...

Subsequently, the collected EEG signal was preprocessed. We then used one hybrid deep learning model based on the convolutional layers and a self-attention mechanism to extract the pertinent EEG ...

1. Reconstruction by interpolation (Erkorkmaz, 2015). 2. Reconstruction by mathematical modeling (Naldi et al., 2017). 3. Reconstruction by deep neural networks (Jin et al., 2017). Among the methods ...

The project employs advanced preprocessing techniques and a deep learning model architecture combining an Autoencoder and a Bidirectional Long Short-Term Memory (LSTM) network to achieve high accuracy ...

Appropriately enough, it’s called deep research. OpenAI said in a blog post published Sunday that this new capability was designed for “people who do intensive knowledge work in areas like ...