This project was a part of my Bachelor’s thesis focused on creating a hybrid model for Electroencephalographic(EEG) in Brain Computer Interfaces(BCI). The objective was to come up with a unique general purpose classifier that can fit multiple EEG datasets efficiently. We began by generating an extended feature set for the datasets. After dynamic feature selection and dimensionality reduction, we juxtapose the performance metrics for various classifiers and seek to build a hybrid classifier. Further, this study is extended to suggest a general purpose tool named EFEACT (EEG Feature extraction and Classification Tool).
- This repository is a subset of another private reposity.
- Generate the features from your EEG dataset using the MATLAB code and save it into a .mat file.
- Runt the tool using main_tool.py file in classifier_tool.
A sample feature extraction mat file is provided with structure and input to the tool.
This list comprises some of the papers used for literature review.
S. Aggarwal, L. Aggarwal, M. S. Rihal and S. Aggarwal, "EEG Based Participant Independent Emotion Classification using Gradient Boosting Machines," 2018 IEEE 8th International Advance Computing Conference (IACC), 2018, pp. 266-271, doi: 10.1109/IADCC.2018.8692106.
