Next Article in Journal
Mechanics of the Tricuspid Valve—From Clinical Diagnosis/Treatment, In-Vivo and In-Vitro Investigations, to Patient-Specific Biomechanical Modeling
Previous Article in Journal
Tissue Level Mechanical Properties and Extracellular Matrix Investigation of the Bovine Jugular Venous Valve Tissue
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle

Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)

1
Narva College, University of Tartu, Narva 20307, Estonia
2
Narva Pahklimae Gymnasium, Narva 20605, Estonia
*
Author to whom correspondence should be addressed.
Bioengineering 2019, 6(2), 46; https://doi.org/10.3390/bioengineering6020046
Received: 26 February 2019 / Revised: 12 May 2019 / Accepted: 14 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning for BCI/BMI)
  |  
PDF [4582 KB, uploaded 17 May 2019]
  |  

Abstract

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset. View Full-Text
Keywords: EEG (electroencephalography); ANN (artificial neural network); BCI (brain-computer interface); BFB (biofeedback); bitronics; Arduino; machine learning EEG (electroencephalography); ANN (artificial neural network); BCI (brain-computer interface); BFB (biofeedback); bitronics; Arduino; machine learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Muhammad, Y.; Vaino, D. Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN). Bioengineering 2019, 6, 46.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Bioengineering EISSN 2306-5354 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top