Reinforced BCI

This is a collection of scripts to perform essential preprocessing steps,

doing denoise and feature extraction of EEG (ERP) data using MATLAB and the EEGLAB toolbox.

The scripts are under current development with no guarantee of proper

functioning. The scripts are published in the hopes of helping people with interests in our experiment to reproduce the steps under their environment, and maybe extend to further improvement in the future. Currently, this project works with Muse S band (cortex TP9, TP10, AF7, AF8) device and is implemented with XDF raw Data. 

EEG data is processed in two steps: First step is the ICA. Raw EEG data that is only preprocessed by ICA Filtering before hand. In our case, we manually do the labelling using the built-in ICA(Independent Component Analysis) tools with EEGLAB, to do the blind signal separation on different brain signal, and remove the component that is irrelevant to our objective. The second step is to do the feature extraction, in this step we would address all the subject data as a batch, and apply DWT with pwelch to all channels of EEG with a static window size, to form the input data. Users can try different window size/processing function/subject number for different configuration(so far modifications can be done by changing the variable value in preprocess_csv.m directly).

As for the model training, in our experiment we use Classification Learner with utilizing minimum redundancy-maximum relevance (MRMR) algorithm in Matlab, which is an application bundled in "Statistics and Machine Learning Toolbox", a built-in toolbox for Matlab R2022a and later version. By taking advantage on this application, we can run training with many models in parallel mode, and make a quick comparison between them.