Skip to content

Latest commit

 

History

11 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 
 
 
 
 

Repository files navigation

EEG_based_drone_navigation_implementation

Author: Nachiketh G

This project is not a real-time implementation; rather, the drone operates based on movement signals predicted by a Brain-Computer Interface (BCI) model. The model is trained on EEG signals corresponding to drone movements using Motor Imagery.

Modeling Approach:

  • SVM Model: Used to evaluate the accuracy of 18 EEG electrodes from the MITASR-21 headset and identify the best 4 electrodes for classification.
  • Random Forest Model: Predicts the event column, which represents different drone movements.
  • The predicted drone movement commands are then sent to an ESP32 to control the drone.

About

This project is not a real-time implementation; instead, the drone operates based on movement signals predicted by the BCI model. The model is trained on BCI signals of drone movements using Motor Imagery.

Resources

Stars

0 stars

Watchers

1 watching

Forks

Releases

Packages

Contributors

Languages