MaltSci 麦伴科研

Appearance

Curated Papers > High-impact authoritative literature on "brain-computer interfaces"

An MEG-based brain-computer interface (BCI). ​

Literature Information ​

DOI10.1016/j.neuroimage.2007.03.019
PMID17475511
JournalNeuroImage
Impact Factor4.5
JCR QuartileQ1
Publication Year2007
Times Cited98
KeywordsBrain-Computer Interface, Electroencephalography, Magnetoencephalography, Self-Control, Motor Cortex
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN1053-8119
Pages581-93
Issue36(3)
AuthorsJürgen Mellinger, Gerwin Schalk, Christoph Braun, Hubert Preissl, Wolfgang Rosenstiel, Niels Birbaumer, Andrea Kübler

TL;DR ​

This study explores the potential of magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) for communication by analyzing voluntary amplitude modulation of sensorimotor rhythms, revealing that participants can achieve significant control over mu rhythms in just 32 minutes of training. The findings indicate that MEG offers improved signal properties that may enhance communication speed for patients with lost voluntary muscle control, thus providing a promising alternative to existing EEG-based BCIs.

Search for more papers on MaltSci.com ​

Brain-Computer Interface · Electroencephalography · Magnetoencephalography · Self-Control · Motor Cortex

Abstract ​

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

MaltSci.com AI Research Service ​

Intelligent ReadingAnswer any question about the paper and explain complex charts and formulas
Locate StatementsFind traces of a specific claim within the paper
Add to KBasePerform data extraction, report drafting, and advanced knowledge mining

Primary Questions Addressed ​

  1. How does the signal-to-noise ratio in MEG compare to that in EEG for BCI applications?
  2. What specific artifacts are encountered in MEG-based BCIs, and how do they affect communication accuracy?
  3. In what ways can the training methods for MEG-based BCIs be optimized to enhance user performance?
  4. How do the spatiotemporal resolution advantages of MEG translate into practical benefits for users compared to EEG-based systems?
  5. What potential applications beyond binary decision-making could be explored with MEG-based BCIs?

Key Findings ​

Research Background and Purpose ​

Brain-Computer Interfaces (BCIs) enable communication through brain activity without muscle involvement, offering vital communication pathways for individuals with severe motor impairments. Traditional EEG-based BCIs, while effective, often require extensive training and have slower communication speeds. This study explores the feasibility of a Magnetoencephalography (MEG)-based BCI that leverages the sensorimotor μ and β rhythms to enhance communication speed and efficiency.

Main Methods/Materials/Experimental Design ​

The study involved six healthy adult participants who were naïve to BCI operation. The experiment consisted of two main phases: a screening session without feedback and training sessions with real-time feedback on μ-rhythm control.

Experimental Workflow:

Mermaid diagram
  • Participants: Six healthy adults (5 male, 1 female; mean age 30).
  • MEG Setup: Used a whole-head MEG system with 151 sensors, recording at 625 Hz.
  • Training Sessions: Participants learned to control their μ-rhythm amplitude through visual feedback of cursor movement on a screen.
  • Real-time Feedback: Provided through a BCI2000 system that computed cursor position based on μ-rhythm amplitude.

Key Results and Findings ​

  • Learning Curves: All participants achieved significant μ-rhythm control, with four achieving over 90% accuracy within 32 minutes of feedback training.
  • Signal Characteristics: The spatial filtering methods developed allowed for enhanced signal-to-noise ratios, and the source localization indicated that the μ-rhythm was primarily generated in the motor cortex for three participants.
  • Performance Metrics: Participants demonstrated a significant increase in accuracy over training sessions, indicating effective learning and adaptation to the BCI system.

Main Conclusions/Significance/Innovation ​

The study successfully demonstrates that an MEG-based BCI can facilitate rapid user training and effective communication through brain activity modulation. The findings suggest that MEG's superior spatial resolution can enhance BCI performance compared to traditional EEG systems. The developed spatial filtering and real-time feedback mechanisms represent significant innovations in BCI technology, potentially leading to more efficient communication aids for individuals with severe motor disabilities.

Research Limitations and Future Directions ​

  • Sample Size: The limited number of participants (n=6) restricts the generalizability of the findings.
  • Head Position Variability: Challenges in maintaining consistent head positioning during sessions may affect signal accuracy.
  • Future Research: Further studies should focus on larger participant groups, exploring long-term training effects, and refining spatial filtering techniques to improve BCI accuracy and reliability.
AspectDetails
Participants6 healthy adults
Training DurationSignificant control achieved within 32 mins
μ-Rhythm Source LocalizationMotor cortex for 3 out of 6 participants
Main FindingsHigh accuracy and rapid training in BCI use
LimitationsSmall sample size, head position variability
Future DirectionsLarger studies, long-term effects, improved filtering techniques

References ​

  1. Mu and beta rhythm topographies during motor imagery and actual movements. - D J McFarland;L A Miner;T M Vaughan;J R Wolpaw - Brain topography (2000)
  2. Brain-computer interface research at the Wadsworth Center. - J R Wolpaw;D J McFarland;T M Vaughan - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
  3. Brain oscillations control hand orthosis in a tetraplegic. - G Pfurtscheller;C Guger;G Müller;G Krausz;C Neuper - Neuroscience letters (2000)
  4. Spatial filter approach for comparison of the forward and inverse problems of electroencephalography and magnetoencephalography. - L A Bradshaw;R S Wijesinghe;J P Wikswo - Annals of biomedical engineering (2001)
  5. Brain-computer interfaces for communication and control. - Jonathan R Wolpaw;Niels Birbaumer;Dennis J McFarland;Gert Pfurtscheller;Theresa M Vaughan - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2002)
  6. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. - Nikolaus Weiskopf;Ralf Veit;Michael Erb;Klaus Mathiak;Wolfgang Grodd;Rainer Goebel;Niels Birbaumer - NeuroImage (2003)
  7. How many people are able to operate an EEG-based brain-computer interface (BCI)? - C Guger;G Edlinger;W Harkam;I Niedermayer;G Pfurtscheller - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2003)
  8. 'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. - Gert Pfurtscheller;Gernot R Müller;Jörg Pfurtscheller;Hans Jürgen Gerner;Rüdiger Rupp - Neuroscience letters (2003)
  9. BCI2000: a general-purpose brain-computer interface (BCI) system. - Gerwin Schalk;Dennis J McFarland;Thilo Hinterberger;Niels Birbaumer;Jonathan R Wolpaw - IEEE transactions on bio-medical engineering (2004)
  10. Localization of realistic cortical activity in MEG using current multipoles. - K Jerbi;S Baillet;J C Mosher;G Nolte;L Garnero;R M Leahy - NeuroImage (2004)

Literatures Citing This Work ​

  1. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. - Ethan Buch;Cornelia Weber;Leonardo G Cohen;Christoph Braun;Michael A Dimyan;Tyler Ard;Jurgen Mellinger;Andrea Caria;Surjo Soekadar;Alissa Fourkas;Niels Birbaumer - Stroke (2008)
  2. Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals. - Harsha Battapady;Peter Lin;Tom Holroyd;Mark Hallett;Xuedong Chen;Ding-Yu Fei;Ou Bai - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2009)
  3. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. - Wei Wang;Jennifer L Collinger;Monica A Perez;Elizabeth C Tyler-Kabara;Leonardo G Cohen;Niels Birbaumer;Steven W Brose;Andrew B Schwartz;Michael L Boninger;Douglas J Weber - Physical medicine and rehabilitation clinics of North America (2010)
  4. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. - Trent J Bradberry;Rodolphe J Gentili;José L Contreras-Vidal - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
  5. Corticospinal beta-band synchronization entails rhythmic gain modulation. - Gijs van Elswijk;Femke Maij;Jan-Mathijs Schoffelen;Sebastiaan Overeem;Dick F Stegeman;Pascal Fries - The Journal of neuroscience : the official journal of the Society for Neuroscience (2010)
  6. A procedure for measuring latencies in brain-computer interfaces. - J Adam Wilson;Jürgen Mellinger;Gerwin Schalk;Justin Williams - IEEE transactions on bio-medical engineering (2010)
  7. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. - Joseph N Mak;Jonathan R Wolpaw - IEEE reviews in biomedical engineering (2009)
  8. Decoding and cortical source localization for intended movement direction with MEG. - Wei Wang;Gustavo P Sudre;Yang Xu;Robert E Kass;Jennifer L Collinger;Alan D Degenhart;Anto I Bagic;Douglas J Weber - Journal of neurophysiology (2010)
  9. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. - J D R Millán;R Rupp;G R Müller-Putz;R Murray-Smith;C Giugliemma;M Tangermann;C Vidaurre;F Cincotti;A Kübler;R Leeb;C Neuper;K-R Müller;D Mattia - Frontiers in neuroscience (2010)
  10. rtMEG: a real-time software interface for magnetoencephalography. - Gustavo Sudre;Lauri Parkkonen;Elizabeth Bock;Sylvain Baillet;Wei Wang;Douglas J Weber - Computational intelligence and neuroscience (2011)

... (88 more literatures)

© 2025 MaltSci - We reshape scientific research with AI technology