Pytorch implementation of "SHNN: A Single-Channel EEG Sleep Staging Model Based on Semi-Supervised Learning". Resources

Mar 1, 2023 · In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences.

Mar 1, 2023 · In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences.

To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to …

Nov 1, 2022 · In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU)...

Jul 24, 2024 · Accurate sleep staging plays a pivotal role in the diagnosis and treatment of sleep-related disorders, yet manual annotation remains a costly task. This study introduces a self-supervised learning approach for sleep staging, requiring minimal labeled data.

Jul 1, 2021 · In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences.

Pytorch implementation of "SHNN: A Single-Channel EEG Sleep Staging Model Based on Semi-Supervised Learning". - Caowenpeng/SHNN

In the SHNN model, we design a multi-scale convolutional neural network (CNN) to extract the features from the single-channel EEG and use a Bi-directional recurrent gating unit (Bi-GRU) to obtain temporal context information of sleep data sequences.

Pytorch implementation of "SHNN: A Single-Channel EEG Sleep Staging Model Based on Semi-Supervised Learning". Python 9 3 - -