Welcome to EEG Familiarity Prediction Repository

This research project is mainly concerned with:

1. Replicate result from this previous paper to build necessary toolset to perform additional research https://pages.ucsd.edu/~desa/EEG_Reveals_Familiarity_2021.pdf
   • Simulation of Accuracy Based on the Reported Value
2. Apply new dataset from this paper to corrobate the findings in the first paper.

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File Structures

• modified-scripts
  • contains the modified MatLab scripts to facilitate our experiment of strictly enforcing zero shrinkage and only measuring the training set error.
  • cal_shrinkage.m
    • Enforces zero shrinkage
  • check_lda_train_reg_auto.m
    • Modified bias calculation
  • lda_apply_prob.m
    • Modified prediction generation processes
• Reports.ipynb
  • A batch report that summarized the current progress of this study.
• mat_preproc.py
  • A preprocessing package dedicates to structuring the multi-classes raw data into a trainable dataset in a binary classification setting.
• LDA_simplest_test.m
  • A simplified MatLab script directly called the modified-scripts. Design to be directly callable from python. See Reports.ipynb of how it works.