/BSS_final_project

Blind Source Separation Final Project, Motor Imagery Classification using CSP and LDA ,University of Tehran Dr.Akhavan Spring 2023

Primary LanguageJupyter Notebook

BSS Final Project: Motor Imagery Classification using CSP and LDA

Overview

This project, Blind Source Separation (BSS) Final Project at the University of Tehran under Dr. Akhavan's supervision (Spring 2023), focuses on classifying motor imagery using Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA). The approach involves analyzing EEG data from the second national Brain-Computer Interface (BCI) competition.

Objective

The primary objective is to classify EEG recordings from 15 individuals, leveraging CSP and LDA. The project entails a comprehensive understanding of EEG data, feature extraction, classification, and performance evaluation using leave-one-out cross-validation.

Dataset

The dataset comprises subj_n.mat files for each subject (n=1 to 15). Detailed dataset information is in Recording.pdf, essential for understanding the recording methods and data structure.

Prerequisites

  • Python 3.x
  • Jupyter Notebook
  • Familiarity with EEG data, signal processing, CSP, and LDA

Installation and Setup

  1. Clone the repository to your local machine.
  2. Ensure Python and Jupyter Notebook are installed.
  3. Navigate to the project directory and launch Jupyter Notebook.
  4. Downloading Dataset and installing requirements done in Jupyter Notebook provided

Acknowledgments

  • Dr. Akhavan for project supervision