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Found 4 papers, 4 papers with code
Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification
The concept of weight freezing revolves around the idea of reducing certain neurons' influence on the decision-making process for a specific EEG task by freezing specific weights in the fully connected layer during the backpropagation process.
Time-space-frequency feature Fusion for 3-channel motor imagery classification
TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification.
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks.
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.
