TY - JOUR
T1 - An MEG-based brain-computer interface (BCI)
AU - Mellinger, Jürgen
AU - Schalk, Gerwin
AU - Braun, Christoph
AU - Preissl, Hubert
AU - Rosenstiel, Wolfgang
AU - Birbaumer, Niels
AU - Kübler, Andrea
PY - 2007/7/1
Y1 - 2007/7/1
N2 - 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 μ and β 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 μ 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.
AB - 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 μ and β 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 μ 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.
KW - μ rhythm
KW - Brain-computer interface
KW - Magnetoencephalography
KW - Real-time feedback
KW - Source localization
UR - http://www.scopus.com/inward/record.url?scp=34250360319&partnerID=8YFLogxK
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U2 - 10.1016/j.neuroimage.2007.03.019
DO - 10.1016/j.neuroimage.2007.03.019
M3 - Article
C2 - 17475511
AN - SCOPUS:34250360319
VL - 36
SP - 581
EP - 593
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 3
ER -