Skip to content

javadkavian/EEG_motor_imagery_analysis

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

38 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Machine Learning Course Project

Comparative Analysis of Machine Learning Algorithms for EEG Signal Classification and Clustering

Abstract

One of the most powerful cognitive processes that in- volves mental simulation of movement without physical execution is motor imagery. In this study, our focus is on extracting and preprocessing EEG signals and feed- ing them to machine-learning models for motor imagery classification and clustering.


Introduction

In this project, we will first get to know EEG signal data and explore different ways to prepare the data, clean it, and remove any unwanted noise, which is common with real-world signals. Then, using techniques for extracting features from these signals that can be useful.

Additionally, we try to classify and cluster the data using the features extracted earlier with different machine-learning algorithms. And finally, the results will be compared and analyzed.


More details

Dataset

Data sets provided by the Berlin BCI group.


Preprocessing

In the preprocessing stage, we employed a variety of techniques, including:

  • Bandpass filtering
  • Common Average Referencing (CAR)
  • Laplacian Filtering
  • Principal Component Analysis (PCA)
  • Normalization

Feature Extraction

For feature extraction, we utilized following algorithms:

  • Independent Component Analysis (ICA)
  • Common Spatial Patterns (CSP)

Classification

In classification section, we explored multiple algorithms, such as:

  • Logistic Regression
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Multi-Layer Perceptron (MLP)
  • AdaBoost
  • XGBoost

Furthermore, to thoroughly evaluate the performance of these classifiers, we calculated an array of metrics, including:

  • Accuracy
  • Confusion Matrix
  • Receiver Operating Characteristic (ROC) Curve

Clustering

Lastly, in the clustering phase, we applied following models:

  • DBSCAN
  • K-means
  • Kernel-based K-means

and analyzed the results using these scores:

  • Silhouette Score
  • Homogeneity Score

For more information, please refer to the project report.

javadkavian & MehdiJmlkh

About

Link to Machine Learning course final project

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published