High gamma-power predicts performance in sensorimotor-rhythm brain–computer interfaces

Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.

[1]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[2]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Katja Kollewe,et al.  Changes of resting state brain networks in amyotrophic lateral sclerosis , 2009 .

[4]  Stephen J. Roberts,et al.  Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation , 2004, IEEE Transactions on Biomedical Engineering.

[5]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[6]  I. Nelken,et al.  Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades , 2008, Neuron.

[7]  William Z Rymer,et al.  Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .

[8]  T O Zander,et al.  Context-aware brain–computer interfaces: exploring the information space of user, technical system and environment , 2012, Journal of neural engineering.

[9]  H. J. Arnold.  Introduction to the Practice of Statistics , 1990 .

[10]  Monica C. Jackson,et al.  Introduction to the Practice of Statistics , 2001 .

[11]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[12]  Richard J. Davidson,et al.  Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG , 2010, NeuroImage.

[13]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[14]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[15]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[16]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[17]  Moritz Grosse-Wentrup,et al.  Using brain–computer interfaces to induce neural plasticity and restore function , 2011, Journal of neural engineering.

[18]  M. Grosse-Wentrup.  Fronto-parietal gamma-oscillations are a cause of performance variation in brain-computer interfacing , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[19]  R.M. Leahy,et al.  Electromagnetic brain imaging using BrainStorm , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[20]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

[21]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[22]  J. Kaiser,et al.  Human gamma-frequency oscillations associated with attention and memory , 2007, Trends in Neurosciences.

[23]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[25]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[26]  Dimitrios Pantazis,et al.  BrainStorm Electromagnetic Imaging Software , 2005 .

[27]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[28]  M. Corbetta,et al.  The Reorienting System of the Human Brain: From Environment to Theory of Mind , 2008, Neuron.

[29]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. , 1998, Brain : a journal of neurology.

[30]  Bernhard Schölkopf,et al.  Causal influence of gamma oscillations on the sensorimotor rhythm , 2011, NeuroImage.

[31]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[33]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[34]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[35]  J. Ojemann,et al.  Quasi-periodic Fluctuations in Default Mode Network Electrophysiology , 2011, The Journal of Neuroscience.

[36]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[37]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[38]  M. Grosse-Wentrup,et al.  Biased feedback in brain-computer interfaces , 2010, Journal of NeuroEngineering and Rehabilitation.

[39]  Klaus-Robert Müller,et al.  Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..