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Article

A brain computer interface with online feedback based on magnetoencephalography

Authors:

Thomas Navin Lal,

Michael Schröder,

N. Jeremy Hill,

Hubert Preissl,

Thilo Hinterberger,

Jürgen Mellinger,

Wolfgang Rosenstiel,

Thomas Hofmann,

Niels Birbaumer,

Bernhard SchölkopfAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 465 - 472

https://doi.org/10.1145/1102351.1102410

Published: 07 August 2005 Publication History

Abstract

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signal-to-noise ratio, is likely to succeed. We apply RCE and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online MEG-based BCI and is therefore a "proof of concept".

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Kim JHeo KShin DNam HWon DJeong JKam T(2024)A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio-spectral-temporal EEG signal decodingExpert Systems with Applications10.1016/j.eswa.2024.123975(123975)Online publication date: Apr-2024
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Ahmad MAhuja K(2022)Role of 5G Communication Along With Blockchain Security in Brain-Computer InterfacingFuturistic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering10.4018/978-1-7998-7433-1.ch004(65-85)Online publication date: 2022
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A brain computer interface with online feedback based on magnetoencephalography
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Information

Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

Zhang GEtemad A(2024)Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33325498:2(1469-1483)Online publication date: Apr-2024
https://doi.org/10.1109/TETCI.2023.3332549
Kim JHeo KShin DNam HWon DJeong JKam T(2024)A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio-spectral-temporal EEG signal decodingExpert Systems with Applications10.1016/j.eswa.2024.123975(123975)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2024.123975
Ahmad MAhuja K(2022)Role of 5G Communication Along With Blockchain Security in Brain-Computer InterfacingFuturistic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering10.4018/978-1-7998-7433-1.ch004(65-85)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-7433-1.ch004
Panachakel JRamakrishnan A(2021)Decoding Covert Speech From EEG-A Comprehensive ReviewFrontiers in Neuroscience10.3389/fnins.2021.64225115Online publication date: 29-Apr-2021
https://doi.org/10.3389/fnins.2021.642251
Wittevrongel BHolmes NBoto EHill RRea MLibert AKhachatryan EVan Hulle MBowtell RBrookes M(2021)Practical real-time MEG-based neural interfacing with optically pumped magnetometersBMC Biology10.1186/s12915-021-01073-619:1Online publication date: 10-Aug-2021
https://doi.org/10.1186/s12915-021-01073-6
Helle LNenonen JLarson ESimola JParkkonen LTaulu S(2021)Extended Signal-Space Separation Method for Improved Interference Suppression in MEGIEEE Transactions on Biomedical Engineering10.1109/TBME.2020.304037368:7(2211-2221)Online publication date: Jul-2021
https://doi.org/10.1109/TBME.2020.3040373
Wittevrongel BHolmes NBoto EHill RRea MLibert AKhachatryan EBowtell RBrookes MVan Hulle M(2021)Optically Pumped Magnetometers for Practical MEG-Based Brain-Computer InterfacingBrain-Computer Interface Research10.1007/978-3-030-79287-9_4(35-46)Online publication date: 29-Aug-2021
https://doi.org/10.1007/978-3-030-79287-9_4
Alexei Vyacheslavovich K(2019)Near-Infrared Optical Technologies in Brain-Computer Interface SystemsNew Frontiers in Brain-Computer Interfaces [Working Title]10.5772/intechopen.83345Online publication date: 9-Jan-2019
https://doi.org/10.5772/intechopen.83345
Baig MAslam NShum H(2019)Filtering techniques for channel selection in motor imagery EEG applications: a surveyArtificial Intelligence Review10.1007/s10462-019-09694-8Online publication date: 28-Feb-2019
https://doi.org/10.1007/s10462-019-09694-8
McClay W(2018)A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL DatabasesDiseases10.3390/diseases60400896:4(89)Online publication date: 28-Sep-2018
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