Eeg Decoding

15 papers with code • 1 benchmarks • 2 datasets

EEG Decoding - extracting useful information directly from EEG data.

Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification

miaozhengqing/weightfreezing 9 Jun 2023

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.

9
09 Jun 2023

Time-space-frequency feature Fusion for 3-channel motor imagery classification

miaozhengqing/lmda-code 4 Apr 2023

TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification.

47
04 Apr 2023

Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation

cecnl/eeg-kd 6 Dec 2022

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.

10
06 Dec 2022

Embedding neurophysiological signals

PierreGtch/embedding_eeg_2022 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering (MetroXRAINE) 2022

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.

3
25 Oct 2022

fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships

kovalalvi/beira 23 Oct 2022

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.

21
23 Oct 2022

MAtt: A Manifold Attention Network for EEG Decoding

cecnl/matt 5 Oct 2022

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs).

49
05 Oct 2022

Physics-inform attention temporal convolutional network for EEG-based motor imagery classification

Altaheri/EEG-ATCNet IEEE Transactions on Industrial Informatics 2022

In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.

130
01 Aug 2022

ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

CECNL/ExBrainable 10 Jan 2022

We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding.

10
10 Jan 2022

Uncertainty Detection and Reduction in Neural Decoding of EEG Signals

tiehangd/ue-eeg 28 Dec 2021

In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.

2
28 Dec 2021