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Found 7 papers, 4 papers with code
SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems.
Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation
Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data.
CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration.
MAtt: A Manifold Attention Network for EEG Decoding
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding.
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning
Significance: This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances.
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication.
