• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace@MEF
  • Fakülteler
  • Mühendislik Fakültesi
  • Bilgisayar Mühendisliği | Computer Engineering
  • MF, BM, Bildiri ve Sunum Koleksiyonu
  • View Item
  •   DSpace@MEF
  • Fakülteler
  • Mühendislik Fakültesi
  • Bilgisayar Mühendisliği | Computer Engineering
  • MF, BM, Bildiri ve Sunum Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
Advanced Search

EMG-based BCI for PiCar Mobilization

Thumbnail

View/Open

Full Text - Article (174.6Kb)

Access

info:eu-repo/semantics/openAccess

Date

2022

Author

Ertekin Efe
Günden Burak Bahri
Yilmaz Yasin, Sayar Alperen, Çakar Tuna, Arslan Suayb S.

Metadata

Show full item record

Citation

Ertekin, E., Gunden, B. B., Yilmaz, Y., Sayar, A., Cakar, T., & Arslan, S. S. (2022). EMG-based BCI for PiCar Mobilization. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.9919502

Abstract

In this study, the main scope was to develop a brain-computer interface (BCI) with the use of PiCar and EEG/ERP devices. Thus, it is aimed to facilitate the lives of people with certain diseases and disabilities. The ultimate goal of this project has been to direct and control a BCI-based PiCar concerning the signals captured via the EEG/ERP device. With the EEG headset, the EMG signals of the gestures (facial expressions) of the participant were captured. With the collected data, filtering and other preprocessing methods were applied to have noise-free signals. In the preprocessing, the detrending method was used to clean the data set which showed a constantly increasing trend, to a certain range, and zero trends. The denoising (Wavelet Denoising) and outlier detection/elimination methods (OneClassSVM) were used for noise elimination. The SMOTE oversampling method was used for data augmentation. Welch's method was used to get band powers from the signals. With the use of augmented data, several machine learning algorithms were applied such as Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, Random forest Classifier, Gradient Boosting Classifier, Multinomial Naive Bayes, Decision tree, K-Nearest Neighbor, and voting classifier. The developed models were used to predict the direction that is passed as an input to PiCar's API. After that, PiCar was controlled concerning the predicted direction with HTTP GET requests. In this project, the OpenBCI headset and the Brainflow library for EEG/EMG signal obtaining and processing were used. Also, the Tkinter library was used for the Graphical user interface and Django for establishing a server on PiCar's brain which is RaspberryPi. © 2022 IEEE.

URI

https://doi.org/10.1109/UBMK55850.2022.9919502
https://hdl.handle.net/20.500.11779/1908

Collections

  • Araştırma Çıktıları, Scopus İndeksli Yayınlar Koleksiyonu [455]
  • MF, BM, Bildiri ve Sunum Koleksiyonu [46]



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Instruction | Guide | Contact |

DSpace@MEF

by OpenAIRE

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsInstitution AuthorTitlesORCIDSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeThis CollectionBy Issue DateAuthorsInstitution AuthorTitlesORCIDSubjectsTypeLanguageDepartmentCategoryPublisherAccess Type

My Account

LoginRegister

Statistics

View Google Analytics Statistics

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Guide|| Instruction || Library || MEF University || OAI-PMH ||

MEF University Library, İstanbul, Turkey
If you find any errors in content please report us

Creative Commons License
MEF University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace@MEF:


DSpace 6.2

tarafından İdeal DSpace hizmetleri çerçevesinde özelleştirilerek kurulmuştur.