An MEG-based brain-computer interface (BCI)

By Jürgen Mellinger, Gerwin Schalk, Christoph Braun, Hubert Preissl, Wolfgang Rosenstiel, Niels Birbaumer, Andrea Kübler. NeuroImage, 2007

466 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9c0e1eb9746f8420959d6547c25909782e0c7743

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “Note, however, that most of the principles we put forward here can also be applied to other types of BCI, either invasive (single-unit activity [25, 69], electrocorticogram [102, 151]) or non-invasive (MEG [81, 111], fMRI [200, 209],”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “In addition to electrical fields emitted by the brain, noninvasively recorded brain magnetic fields have also provided signals for BMIs (104, 413, 458, 541).”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Although MEG provides better spatiotemporal resolution as compared to EEG, the magnetic field created by the brain is very small, requiring costly, stationary recording equipment (Mellinger et al., 2007; Corsi et al., 2019).”
Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general (2011, unknown): “Relevant non-invasive measures are functional magnetic resonance imaging (fMRI) [5–7], magnetoencephalography (MEG) [8, 9] and functional near-infrared spectrography (fNIRS) [10]. fMRI and MEG share the major drawbacks of being complex in application and unsuitable for long-term use.”
Brain-Computer Interfaces for Communication and Control (2011, unknown): “In addition to electrophysiological measures, researchers have also demonstrated the feasibility of magneto-encephalography (MEG), 20 functional magnetic resonance imaging (fMRI), 28 and near-infrared systems (fNIR).”
Brain Computer Interfaces, a Review (2012, background): “MEG provides signals with higher spatiotemporal resolution than EEG, which reduces the training time needed to control a BCI and speeds up reliable communications [50].”
fNIRS-based brain-computer interfaces: a review (2015, background): “Unlike EEG and MEG, its data are not much susceptible to electrical noise, since it is an optical imaging modality. fNIRS measures the blood ﬂow changes in the local capillary network caused by neuron ﬁrings.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, methodology): “Recent BCI research using MEG signals demonstrated reliable self-control of sensorimotor rhythm amplitude [51] and satisfactory two-dimensional BCI control [53].”