A Decoding algorithm for Non-invasive SSVEP-based Drone Flight Control

Abdelhadi Hireche, Yasmine Zennaia, Redouane Ayad, Abdelkader Nasreddine Belkacem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Many advanced researches on natural user interfaces methods based on user-centered design have been using speech, gestures and vision to interact with environment and/or control internet of things (IoT) devices. Brain computer interfaces (BCIs) technology could make this interaction/control more natural, faster, and reliable, and effective. In this paper, we propose a decoding algorithm for controlling a drone in a three-dimensional (3D) space using steady state visually evoked potential (SSVEP)-based BCI modality. SSVEP-based BCI has the great potential for use in virtual reality environment, which enables the user to control the drone using his/her brain activity in an first-person-view mode. Therefore, the user will be in a full control over the flight using BCI system by commanding the drone to take off, land, go forward, stop, and turn right/left. This system yields a super convenient way for normal people with no prior experience to interact with the drone and control a flight mission in a little to no time, over traditional manual control which takes longer time to learn and perfect. in the decoding phase, a various convolutional neural networks (CNN) models were built to accommodate different control criteria such as the generality of the model. This proposed EEG-decode-pipeline has been implemented on an open-source data-set which consists of 8-channel EEG data from 10 subjects performing 12 target SSVEP-based BCI task. A high multi-class BCI classification results were achieved with an accuracy ranging around 80-90% for performing a successful online simulation of the drone control.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3616-3623
Number of pages8
ISBN (Electronic)9781665401265
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: Dec 9 2021Dec 12 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/9/2112/12/21

Keywords

  • Brain Computer Interface (BCI)
  • Drone control
  • Electroencephalography (EEG)
  • Steady-State Visual Evoked Potential (SSVEP).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'A Decoding algorithm for Non-invasive SSVEP-based Drone Flight Control'. Together they form a unique fingerprint.

Cite this