This project is a compartitive study of different ML algorithms to find out the best sutiable Algorithm for BCI.
Brain-Computer Interface is a system that allows users to interact with the computer, where the device predicts the abstract aspects of cognitive state with brain signals such as Electroencephalography (EEG).
- To overcome the pathological shortcomings of the human body, such as neuromuscular disorders, through technology.
- Helping motor deficit patients by using the neuroplastic through BCI.
- I have taken the approach of utilizing deep learning algorithms in order to attempt to reduce time delay caused by software or algorithms.
For this project I did a wide literature review and shortlisted three algorithms namely Support Vector Machine(SVM), Random Forest Classifier, Gradient Boost Classifier. These algorithms were shortlisted based on their previous result, easy-to-use code, less inference time.
- MATLAB:- It is used to extract the useful values from the EEG dataset and the combine the datased and write it to a CSV file.
- Python:- It is used to write code for all the ML algorithms for getting the accuracy the achieved.
For more information kindly refer to the attached PPT. Find CSV file for dataset here:- Dataset