• Corpus ID: 245854082

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

@article{Huang2022ExBrainableAO,
  title={ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation},
  author={Ya-Lin Huang and Chia-Ying Hsieh and Jian-Xue Huang and Chunshan Wei},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.04065}
}
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of… 
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References

SHOWING 1-10 OF 27 REFERENCES
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
TLDR
The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
Spatial Component-wise Convolutional Network (SCCNet) for Motor-Imagery EEG Classification
TLDR
Through a series of optimization and validation, this work shows the superiority of SCCNet in MI EEG classification, outperforming other existing CNNs.
EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces
TLDR
The results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks, suggesting that the observed performances were not due to artifact or noise sources in the data.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
TLDR
This paper shows how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration.
Deep learning with convolutional neural networks for EEG decoding and visualization
TLDR
This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
MEG and EEG data analysis with MNE-Python
TLDR
MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
Where Does EEG Come From and What Does It Mean?
Noninvasive Source Localization of Interictal EEG Spikes: Effects of Signal-to-Noise Ratio and Averaging
  • T. Bast, T. Boppel, M. Scherg
  • Computer Science
    Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society
  • 2006
TLDR
A dominant role of the SNR is found on the estimated extent and improvement by scatterplots based on the dipole localization of randomly sampled subaverages based on a comparison of the localization scatter of 100 single spikes in 25 epilepsy patients with 1000 different subaversages computed by random sampling and bootstrapping.
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
TLDR
A novel filter bank common spatial pattern (FBCSP) is proposed to perform autonomous selection of key temporal-spatial discriminative EEG characteristics and shows that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
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