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README.md

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-# Decoding_EEG
+## Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset
+
+### Abstract
+
+This paper focuses on classifying emotions on the valence-arousal plane using various
+feature extraction, feature selection and machine learning techniques. Emotion
+classification using EEG data and machine learning techniques has been on the rise in
+the recent past. We evaluate different feature extraction techniques, feature selection
+techniques and propose the optimal set of features and electrodes for emotion
+recognition. The images from the OASIS image dataset were used for eliciting the
+valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc
+X mobile EEG headset. The analysis is additionally carried out on publicly available
+datasets: DEAP and DREAMER. We propose a novel feature ranking technique and
+incremental learning approach to analyze the dependence of performance on the number
+of participants. Leave-one-subject-out cross-validation was carried out to identify
+subject bias in emotion elicitation patterns. The importance of different electrode
+locations was calculated, which could be used for designing a headset for emotion
+recognition. Our study achieved root mean square errors (RMSE) of less than 0.75 on
+DREAMER, 1.76 on DEAP, and 2.39 on our dataset.
+
+## Made With
+
+* Python 3
+* EEGExtract.py
+* Scikit-learn
+* RAPIDS cuML
+* Numpy
+* Pandas
+* Matplotlib
+
+## Usage
+
+Please make sure the following files are present before executing the code for this project:
+
+1. ImportUtils.py
+2. EEGExtract.py
+3. Preprocess.py
+4. utils.py
+5. EpochedFeatures.py
+6. feature_extraction_25gb_ram
+7. feature_extraction_25GB_RAM_DASM_RASM.py
+8. feature_select_main.py
+9. incremental_learning_deap.py
+10. incremental_learning_dreamer.py
+11. incremental_learning_oasis.py
+12. incremental_learning_final_plots.py
+13. run_scripts_incremental_learning.py
+14. TopNByFSMethods.py
+15. TopNByClassifier.py
+16. 8.5_cross_validate.py
+
+Note: For loading dataset, load_dataset.ipynb was used to load EEG data from headset recordings to NumPy array.
+
+## To perform Electrode-Feature Analysis
+
+For Example: To perform electrode and feature analysis with user-defined parameters:
+
+* Dataset = DREAMER
+* Window Length = 1 sec
+* Stride = 1 sec
+*Sampling Frequency =  128
+
+* ML Model = Support Vector Regressor [SVR()]
+
+* Target Label = 0 (for valence)
+* Approach Used = byfs (by using Sklearn Feature Selection Methods)
+* ml_algo = regression
+* top = e (Electrodes)
+* fs_method =  SelectKBest
+
+```bash
+python3 feature_select_main.py --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model svr --label 0 --approach byfs  --ml_algo regression --top e  --fs_method SelectKBest
+```
+
+## For Incremental Learning
+
+1. Run run_scripts_incremental_learning.py
+2. For plotting the incremental learning results, run incremental_learning_final_plots.py
+ 
+## Contributing
+Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
+
+Please make sure to update tests as appropriate.