Brain-computer interface-based humanoid control: A review

Vinay Chamola, Ankur Vineet, Anand Nayyar, Eklas Hossain

Research output: Contribution to journalReview articlepeer-review

63 Scopus citations

Abstract

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.

Original languageEnglish
Article number3620
Pages (from-to)1-23
Number of pages23
JournalSensors (Switzerland)
Volume20
Issue number13
DOIs
StatePublished - Jul 2020

Keywords

  • Biological feedback
  • Brain-computer interface (BCI)
  • Data fusion
  • Electroencephalography (EEG)
  • Nao humanoid
  • P300

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