Eeg Decoding
15 papers with code • 1 benchmarks • 2 datasets
EEG Decoding - extracting useful information directly from EEG data.
Latest papers
Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity
Moreover, SVG generates a uniform distribution and stabilizes the training process of models.
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.
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.
Embedding neurophysiological signals
Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e. g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e. g., in a demanding work environment.
fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships
The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases.
MAtt: A Manifold Attention Network for EEG Decoding
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).
Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.
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.
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.