Download
bioengineering-06-00046.pdf 4,47MB
WeightNameValue
1000 Titel
  • Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)
1000 Autor/in
  1. Muhammad, Yar |
  2. Vaino, Daniil |
1000 Erscheinungsjahr 2019
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2019-05-17
1000 Erschienen in
1000 Quellenangabe
  • 6(2):46
1000 Copyrightjahr
  • 2019
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3390/bioengineering6020046 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal BCI (brain-computer interface)
lokal machine learning
lokal bitronics
lokal ANN (artificial neural network)
lokal EEG (electroencephalography)
lokal BFB (biofeedback)
lokal Arduino
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/TXVoYW1tYWQsIFlhcg==|https://frl.publisso.de/adhoc/uri/VmFpbm8sIERhbmlpbA==
1000 Label
1000 Förderer
  1. Eesti Teadusagentuur |
  2. Tartu Ülikool |
1000 Fördernummer
  1. -
  2. -
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Eesti Teadusagentuur |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Tartu Ülikool |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6419916.rdf
1000 Erstellt am 2020-04-09T10:45:43.109+0200
1000 Erstellt von 21
1000 beschreibt frl:6419916
1000 Bearbeitet von 21
1000 Zuletzt bearbeitet Thu Apr 09 10:46:57 CEST 2020
1000 Objekt bearb. Thu Apr 09 10:46:44 CEST 2020
1000 Vgl. frl:6419916
1000 Oai Id
  1. oai:frl.publisso.de:frl:6419916 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source