Application of EEG for multimodal human-machine interface

Jangwoo Park, Il Woo, Shin Suk Park

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

6 Citations (Scopus)

Abstract

There are many input modalities for human-machine interface (HMI). Brain-signal that is one of biosignal has been studied as an input modality for HMI. Brain-signal based HMI can help disabled people to communicate with a machine using the brain's electrical activity. this study is focuses on usability of the EEG-based HMI's for available tools in real life and possibility of the EEG signal as input modality of multimodal interface. This study attempt to explore the electroencephalogram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks, such as self-concentration, self-arithmetic (non-display), self-arithmetic (show display) and eye-closing. EEG signals are recorded while subjects perform each task on Fz, Cz, Pz. The receiver operating characteristic (ROC) curve analysis is to determine the threshold on each task. Rate of distinction range is 50.32% ∼ 56.77% with the threshold about self-arithmetic and 71.67%∼78.33% with the threshold about eye-closing. There are some meaningful results about threshold, self-arithmetic and eye-close activity. It can be used for brain-machine interface and multi-modal interface.

Original languageEnglish
Title of host publicationInternational Conference on Control, Automation and Systems
Pages1869-1873
Number of pages5
Publication statusPublished - 2012 Dec 1
Event2012 12th International Conference on Control, Automation and Systems, ICCAS 2012 - Jeju, Korea, Republic of
Duration: 2012 Oct 172012 Oct 21

Other

Other2012 12th International Conference on Control, Automation and Systems, ICCAS 2012
CountryKorea, Republic of
CityJeju
Period12/10/1712/10/21

Fingerprint

Electroencephalography
Brain
Plant shutdowns
Display devices
Experiments

Keywords

  • Electroencephalogram(EEG)
  • Human-machine interface (HMI)
  • Mental arithmetic
  • Receiver operating characteristic (ROC)
  • Task difficulty

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Park, J., Woo, I., & Park, S. S. (2012). Application of EEG for multimodal human-machine interface. In International Conference on Control, Automation and Systems (pp. 1869-1873). [6393151]

Application of EEG for multimodal human-machine interface. / Park, Jangwoo; Woo, Il; Park, Shin Suk.

International Conference on Control, Automation and Systems. 2012. p. 1869-1873 6393151.

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

Park, J, Woo, I & Park, SS 2012, Application of EEG for multimodal human-machine interface. in International Conference on Control, Automation and Systems., 6393151, pp. 1869-1873, 2012 12th International Conference on Control, Automation and Systems, ICCAS 2012, Jeju, Korea, Republic of, 12/10/17.
Park J, Woo I, Park SS. Application of EEG for multimodal human-machine interface. In International Conference on Control, Automation and Systems. 2012. p. 1869-1873. 6393151
Park, Jangwoo ; Woo, Il ; Park, Shin Suk. / Application of EEG for multimodal human-machine interface. International Conference on Control, Automation and Systems. 2012. pp. 1869-1873
@inproceedings{843900f81d894d588fcd6db46b19ad1b,
title = "Application of EEG for multimodal human-machine interface",
abstract = "There are many input modalities for human-machine interface (HMI). Brain-signal that is one of biosignal has been studied as an input modality for HMI. Brain-signal based HMI can help disabled people to communicate with a machine using the brain's electrical activity. this study is focuses on usability of the EEG-based HMI's for available tools in real life and possibility of the EEG signal as input modality of multimodal interface. This study attempt to explore the electroencephalogram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks, such as self-concentration, self-arithmetic (non-display), self-arithmetic (show display) and eye-closing. EEG signals are recorded while subjects perform each task on Fz, Cz, Pz. The receiver operating characteristic (ROC) curve analysis is to determine the threshold on each task. Rate of distinction range is 50.32{\%} ∼ 56.77{\%} with the threshold about self-arithmetic and 71.67{\%}∼78.33{\%} with the threshold about eye-closing. There are some meaningful results about threshold, self-arithmetic and eye-close activity. It can be used for brain-machine interface and multi-modal interface.",
keywords = "Electroencephalogram(EEG), Human-machine interface (HMI), Mental arithmetic, Receiver operating characteristic (ROC), Task difficulty",
author = "Jangwoo Park and Il Woo and Park, {Shin Suk}",
year = "2012",
month = "12",
day = "1",
language = "English",
isbn = "9781467322478",
pages = "1869--1873",
booktitle = "International Conference on Control, Automation and Systems",

}

TY - GEN

T1 - Application of EEG for multimodal human-machine interface

AU - Park, Jangwoo

AU - Woo, Il

AU - Park, Shin Suk

PY - 2012/12/1

Y1 - 2012/12/1

N2 - There are many input modalities for human-machine interface (HMI). Brain-signal that is one of biosignal has been studied as an input modality for HMI. Brain-signal based HMI can help disabled people to communicate with a machine using the brain's electrical activity. this study is focuses on usability of the EEG-based HMI's for available tools in real life and possibility of the EEG signal as input modality of multimodal interface. This study attempt to explore the electroencephalogram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks, such as self-concentration, self-arithmetic (non-display), self-arithmetic (show display) and eye-closing. EEG signals are recorded while subjects perform each task on Fz, Cz, Pz. The receiver operating characteristic (ROC) curve analysis is to determine the threshold on each task. Rate of distinction range is 50.32% ∼ 56.77% with the threshold about self-arithmetic and 71.67%∼78.33% with the threshold about eye-closing. There are some meaningful results about threshold, self-arithmetic and eye-close activity. It can be used for brain-machine interface and multi-modal interface.

AB - There are many input modalities for human-machine interface (HMI). Brain-signal that is one of biosignal has been studied as an input modality for HMI. Brain-signal based HMI can help disabled people to communicate with a machine using the brain's electrical activity. this study is focuses on usability of the EEG-based HMI's for available tools in real life and possibility of the EEG signal as input modality of multimodal interface. This study attempt to explore the electroencephalogram (EEG) signal measurement and analysis methods related to concentration for multimodal Interface. The experiments have been performed with various tasks, such as self-concentration, self-arithmetic (non-display), self-arithmetic (show display) and eye-closing. EEG signals are recorded while subjects perform each task on Fz, Cz, Pz. The receiver operating characteristic (ROC) curve analysis is to determine the threshold on each task. Rate of distinction range is 50.32% ∼ 56.77% with the threshold about self-arithmetic and 71.67%∼78.33% with the threshold about eye-closing. There are some meaningful results about threshold, self-arithmetic and eye-close activity. It can be used for brain-machine interface and multi-modal interface.

KW - Electroencephalogram(EEG)

KW - Human-machine interface (HMI)

KW - Mental arithmetic

KW - Receiver operating characteristic (ROC)

KW - Task difficulty

UR - http://www.scopus.com/inward/record.url?scp=84872550074&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872550074&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84872550074

SN - 9781467322478

SP - 1869

EP - 1873

BT - International Conference on Control, Automation and Systems

ER -