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 
+