Added augmentation methods

Papers.md

@@ -44,11 +44,6 @@ Models like GoogleNet and AlexNet have been used, where AlexNet outperformed Goo
 ## A generic framework for adaptive EEG-based BCI training and operation
 https://arxiv.org/abs/1707.07935
 
-## EEG Data Augmentation Method for Identity Recognition Based on Spatial–Temporal Generating Adversarial Network
-https://www.mdpi.com/2079-9292/13/21/4310
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-Authors propose end-to-end EEG data augmentation method  based on spatial-temporal generative adversarial(STGAN) network. Discriminator uses temporal feature encoding (todo learn about it more [here](https://medium.com/@raphael.schoenenberger_95380/encoding-temporal-features-part-1-f26d08feebd8)) and a spatial feature encoder in parallel. Good for global dependencies across channels and time of EEG. GAN improves quality and diversity of augmented EEG data. Conducted on BCI-IV2A dataset. Frechet inception distance was used to evaluate data quality. Compared with deep learning models: EEGNET, ShalowConvNet and DeepConvNet. Approach with STGAN  was better in terms of data quality.
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 ## Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method
 https://www.nature.com/articles/s41598-022-07992-w
 
@@ -80,7 +75,7 @@ Somehow we could leverage methods proposed here.
 ## Data augmentation for deep-learning-based electroencephalography
 https://www.sciencedirect.com/science/article/pii/S0165027020303083?via%3Dihub
 Authors try to augment EEG data for better results using them in deep learning. 
-It is only review. This papers discusses methods for DA:
+It is only review. This papers discusses methods for data augmentation:
 * noise addition
 * GAN
 * sliding window
@@ -164,6 +159,8 @@ Variations of VAE:
 * VQ-vae - provide discrete latent space for sharper reconstructions
 
 
+
+
 TODO:
 - find augmentation methods worth of trying
 - find example architecture for classification BCI