xnovos13/research_augmentation_EEG
2
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added on augmentation info

Browse Source
This commit is contained in:
Juraj Novosad
2025-06-16 14:15:05 +02:00
parent 823134efd8
commit 82abe6fb6c
4 changed files with 43 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
Organizing dataset.md
View File

@ -35,3 +35,21 @@ Adaptive training - train on all subjects and half data of one subject. Test on
Down-sampled to 100 Hz. Only testing generalizability, using the adaptive training with and without augmented data.
### [[Augmentation methods#Augmenting The Size of EEG datasets Using Generative Adversarial Networks (2018)]]
* Evaluation using 5 fold cross-validation on dataset PhysioNet against AutoEncoders and VAE. Using metric reconstruction error.
* Assesing impact of RGAN with different classification models. Evaluating classifcation accuracy on deep feed-forward NN, SVM, random forest tree.
## [[Augmentation methods#Data augmentation strategies for EEG-based motor imagery decoding (2022)]]
Used datasets:
* https://academic.oup.com/gigascience/article/6/7/gix034/3796323
* https://www.nature.com/articles/sdata2018211
For now I don't know where to get raw data of those datasets
Data processing:
* Bandpass filter 1-40 Hz
* Baseline correction was performed with the first 200ms pre-cue. Subtract average of eeg signal before the cue
* Artifact correction, oculograph and myograph. Slightly different parameters for each dataset
* Data re-referencing to average to improve the signal-to-noise ratio. The signal at each channel is re-referenced to the average signal across all electrodes.
* Used [[Papers#Autoreject Automated artifact rejection for MEG and EEG data]]
Dataset was split in ratio 70:12:18 between train:validation:test.