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
17. args_eeg.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

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.