Added on augmentation info

Augmentation methods.md

@@ -63,6 +63,9 @@ TODO: very interesting worth reading more times.
 ## Augmenting The Size of EEG datasets Using Generative Adversarial Networks (2018)
 Explained here: [[Augmentation methods#EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network]]
 Link: https://ieeexplore.ieee.org/abstract/document/8489727
+Authors propose architecture of recurrent generative adversarial network (RGAN). The main feature is using recurrent neural network in generator.
+![Image](images/Pasted_image_20250616105342.png)
+
 
 ## Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface(2020)
 Explained here: [[Augmentation methods#EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network]]
@@ -89,7 +92,22 @@ Down-sampled to 100 Hz. Only testing generalizability, using the adaptive traini
 **What we can do**: Extract something like action-specific feature vector and feed it to generator along with noise so it will generate new data specific for each movement. 
 Extracting that specific vector can be done with (now only thinking) some clustering or find method which would find discriminative features given labels. 
 
- 
+
+## Data augmentation strategies for EEG-based motor imagery decoding (2022)
+Link: https://www.cell.com/heliyon/fulltext/S2405-8440(22)01528-6
+Good introduction, can be used to source citation to intro.
+Evaluation of these augmentation techniques:
+* Averaging randomly selected trials - no good
+* Recombining time slices of randomly selected trials - no good
+* Recombining frequency slices of randomly selected data - no good 
+* Gaussian noise addition
+* Cropping
+* Variational autoencoders data synthesis. Kullback-Leibler (KL) divergence and mean square reconstruction loss.
+Metrics for evaluation of generated data: 
+* Accuracy of prediction
+* Frechet inception distance
+* t-distributed stochastic neighbor embedding plots 
+
 
 
 

Organizing dataset.md

@@ -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. 
+

Papers.md

@@ -160,6 +160,9 @@ Variations of VAE:
 
 
 
+## Autoreject: Automated artifact rejection for MEG and EEG data
+Link: https://www.sciencedirect.com/science/article/pii/S1053811917305013
+
 
 TODO:
 - find augmentation methods worth of trying