Added todo list specification

Papers.md

@@ -115,6 +115,55 @@ https://pmc.ncbi.nlm.nih.gov/articles/PMC4045570/
 Review of methods used to extract features from EEG data. Algorithmic methods, not much of neural is described.
 
 
+## Usage of variational autoencoders
+### Key Studies on VAEs and AEs in EEG Feature Extraction
+
+1. **VAEEG: Variational Auto-Encoder for EEG Representation**
+    
+    - **Overview**: Introduces a self-supervised VAE model, VAEEG, designed to extract concise and informative representations from EEG data across separate frequency bands.
+        
+    - **Applications**: Demonstrated effectiveness in clinical tasks such as pediatric brain development assessment, epileptic seizure detection, and sleep stage classification.
+        
+    - **Findings**: VAEEG achieved superior reconstruction performance and enhanced downstream classification tasks, indicating its potential as a robust feature extractor for EEG signals. [sciencedirect.com+1pubmed.ncbi.nlm.nih.gov+1](https://www.sciencedirect.com/science/article/pii/S1053811924004439?utm_source=chatgpt.com)
+        
+2. **hvEEGNet: Hierarchical VAE for EEG Data**
+    
+    - **Overview**: Proposes two VAE models, vEEGNet-ver3 and hvEEGNet, incorporating EEGNet-based encoders and a dynamic time warping loss function.
+        
+    - **Applications**: Tested on the BCI Competition IV Dataset 2a, focusing on high-fidelity EEG reconstruction.
+        
+    - **Findings**: hvEEGNet outperformed previous solutions in reconstructing EEG data, suggesting its utility in anomaly detection and as a feature extractor for classification tasks. [imiens.org+4arxiv.org+4pubmed.ncbi.nlm.nih.gov+4](https://arxiv.org/abs/2312.00799?utm_source=chatgpt.com)
+        
+3. **EEG2Vec: Learning Affective EEG Representations via VAEs**
+    
+    - **Overview**: Develops a conditional VAE framework, EEG2Vec, to learn generative-discriminative representations from EEG data.
+        
+    - **Applications**: Focused on emotion recognition, achieving robust classification performance and the ability to generate synthetic EEG data resembling real inputs.
+        
+    - **Findings**: Demonstrated the model's suitability for unsupervised EEG modeling and potential in generating artificial training data. [arxiv.org](https://arxiv.org/abs/2207.08002?utm_source=chatgpt.com)
+        
+4. **CNN-VAE Framework for Motor Imagery Classification**
+    
+    - **Overview**: Combines Convolutional Neural Networks (CNNs) with VAEs to classify motor imagery EEG signals.
+        
+    - **Applications**: Applied to the BCI Competition IV Dataset 2b, focusing on motor imagery tasks.
+        
+    - **Findings**: The CNN-VAE framework outperformed existing methods, indicating the effectiveness of integrating CNNs with VAEs for feature extraction and classification in EEG-based BCIs. [imiens.org+5ouci.dntb.gov.ua+5pubmed.ncbi.nlm.nih.gov+5](https://ouci.dntb.gov.ua/en/works/lDXm8RZl/?utm_source=chatgpt.com)[ouci.dntb.gov.ua+5link.springer.com+5pubmed.ncbi.nlm.nih.gov+5](https://link.springer.com/article/10.1007/s11042-024-19850-0?utm_source=chatgpt.com)[pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC6387242/?utm_source=chatgpt.com)
+        
+5. **Unsupervised Feature Extraction with Autoencoders for Multiclass Motor Imagery BCI**
+    
+    - **Overview**: Utilizes autoencoders for unsupervised feature extraction in multiclass motor imagery EEG classification.
+        
+    - **Applications**: Focused on enhancing classification performance in BCI systems.
+        
+    - **Findings**: The approach improved classification accuracy, demonstrating the potential of autoencoders in extracting meaningful features from EEG data. [ouci.dntb.gov.ua](https://ouci.dntb.gov.ua/en/works/lDXm8RZl/?utm_source=chatgpt.com)[pubmed.ncbi.nlm.nih.gov+1ouci.dntb.gov.ua+1](https://pubmed.ncbi.nlm.nih.gov/32982703/?utm_source=chatgpt.com)
+
+Variations of VAE: 
+* CVAE - conditional vae. Class specific image generation
+* Beta-vae - Tunable parameter to control tradeof between reconstruction quality and disentanglement
+* VQ-vae - provide discrete latent space for sharper reconstructions
+
+
 TODO:
 - find augmentation methods worth of trying
 - find example architecture for classification BCI