Using rest class and control paradigms for brain computer interfacing

Siamac Fazli, Márton Danóczy, Florin Popescu, Benjamin Blankertz, Klaus Muller

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

5 Citations (Scopus)

Abstract

The use of Electro-encephalography (EEG) for Brain Computer Interface (BCI) provides a cost-efficient, safe, portable and easy to use BCI for both healthy users and the disabled. This paper will first briefly review some of the current challenges in BCI research and then discuss two of them in more detail, namely modeling the "no command" (rest) state and the use of control paradigms in BCI. For effective prosthetic control of a BCI system or when employing BCI as an additional control-channel for gaming or other generic man machine interfacing, a user should not be required to be continuously in an active state, as is current practice. In our approach, the signals are first transduced by computing Gaussian probability distributions of signal features for each mental state, then a prior distribution of idle-state is inferred and subsequently adapted during use of the BCI. We furthermore investigate the effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, and outline the strategies used by 2 subjects to achieve idle state BCI control.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages651-665
Number of pages15
Volume5517 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2009 Aug 20
Externally publishedYes
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: 2009 Jun 102009 Jun 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5517 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Work-Conference on Artificial Neural Networks, IWANN 2009
CountrySpain
CitySalamanca
Period09/6/1009/6/12

Fingerprint

Brain computer interface
Brain
Paradigm
Class
Electroencephalography
Prosthetics
Gaming
Dynamic Control
Probability distributions
Prior distribution
Gaussian distribution
Probability Distribution
Computing

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Fazli, S., Danóczy, M., Popescu, F., Blankertz, B., & Muller, K. (2009). Using rest class and control paradigms for brain computer interfacing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5517 LNCS, pp. 651-665). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-02478-8_82

Using rest class and control paradigms for brain computer interfacing. / Fazli, Siamac; Danóczy, Márton; Popescu, Florin; Blankertz, Benjamin; Muller, Klaus.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5517 LNCS PART 1. ed. 2009. p. 651-665 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1).

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

Fazli, S, Danóczy, M, Popescu, F, Blankertz, B & Muller, K 2009, Using rest class and control paradigms for brain computer interfacing. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 5517 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5517 LNCS, pp. 651-665, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, 09/6/10. https://doi.org/10.1007/978-3-642-02478-8_82
Fazli S, Danóczy M, Popescu F, Blankertz B, Muller K. Using rest class and control paradigms for brain computer interfacing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 5517 LNCS. 2009. p. 651-665. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-02478-8_82
Fazli, Siamac ; Danóczy, Márton ; Popescu, Florin ; Blankertz, Benjamin ; Muller, Klaus. / Using rest class and control paradigms for brain computer interfacing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5517 LNCS PART 1. ed. 2009. pp. 651-665 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
@inproceedings{c3ee1b2454244c2cb1829e578c80df45,
title = "Using rest class and control paradigms for brain computer interfacing",
abstract = "The use of Electro-encephalography (EEG) for Brain Computer Interface (BCI) provides a cost-efficient, safe, portable and easy to use BCI for both healthy users and the disabled. This paper will first briefly review some of the current challenges in BCI research and then discuss two of them in more detail, namely modeling the {"}no command{"} (rest) state and the use of control paradigms in BCI. For effective prosthetic control of a BCI system or when employing BCI as an additional control-channel for gaming or other generic man machine interfacing, a user should not be required to be continuously in an active state, as is current practice. In our approach, the signals are first transduced by computing Gaussian probability distributions of signal features for each mental state, then a prior distribution of idle-state is inferred and subsequently adapted during use of the BCI. We furthermore investigate the effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, and outline the strategies used by 2 subjects to achieve idle state BCI control.",
author = "Siamac Fazli and M{\'a}rton Dan{\'o}czy and Florin Popescu and Benjamin Blankertz and Klaus Muller",
year = "2009",
month = "8",
day = "20",
doi = "10.1007/978-3-642-02478-8_82",
language = "English",
isbn = "3642024777",
volume = "5517 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "651--665",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 1",

}

TY - GEN

T1 - Using rest class and control paradigms for brain computer interfacing

AU - Fazli, Siamac

AU - Danóczy, Márton

AU - Popescu, Florin

AU - Blankertz, Benjamin

AU - Muller, Klaus

PY - 2009/8/20

Y1 - 2009/8/20

N2 - The use of Electro-encephalography (EEG) for Brain Computer Interface (BCI) provides a cost-efficient, safe, portable and easy to use BCI for both healthy users and the disabled. This paper will first briefly review some of the current challenges in BCI research and then discuss two of them in more detail, namely modeling the "no command" (rest) state and the use of control paradigms in BCI. For effective prosthetic control of a BCI system or when employing BCI as an additional control-channel for gaming or other generic man machine interfacing, a user should not be required to be continuously in an active state, as is current practice. In our approach, the signals are first transduced by computing Gaussian probability distributions of signal features for each mental state, then a prior distribution of idle-state is inferred and subsequently adapted during use of the BCI. We furthermore investigate the effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, and outline the strategies used by 2 subjects to achieve idle state BCI control.

AB - The use of Electro-encephalography (EEG) for Brain Computer Interface (BCI) provides a cost-efficient, safe, portable and easy to use BCI for both healthy users and the disabled. This paper will first briefly review some of the current challenges in BCI research and then discuss two of them in more detail, namely modeling the "no command" (rest) state and the use of control paradigms in BCI. For effective prosthetic control of a BCI system or when employing BCI as an additional control-channel for gaming or other generic man machine interfacing, a user should not be required to be continuously in an active state, as is current practice. In our approach, the signals are first transduced by computing Gaussian probability distributions of signal features for each mental state, then a prior distribution of idle-state is inferred and subsequently adapted during use of the BCI. We furthermore investigate the effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, and outline the strategies used by 2 subjects to achieve idle state BCI control.

UR - http://www.scopus.com/inward/record.url?scp=68749091412&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=68749091412&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-02478-8_82

DO - 10.1007/978-3-642-02478-8_82

M3 - Conference contribution

SN - 3642024777

SN - 9783642024771

VL - 5517 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 651

EP - 665

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -