Causal Influence of Gamma Oscillations on Performance in Brain-Computer Interfaces




Background and Objective: While machine learning approaches have led to tremendous advances in brain-computer interfaces (BCIs) in recent years (cf. [1]), there still exists a large variation in performance across subjects. Furthermore, a significant proportion of subjects appears incapable of achieving above chance-level classification accuracy [2], which to date includes all subjects in a completely locked-in state that have been trained in BCI control. Understanding the reasons for this variation in performance arguably constitutes one of the most fundamental open questions in research on BCIs. Methods & Results Using a machine learning approach, we derive a trial-wise measure of how well EEG recordings can be classified as either left- or right-hand motor imagery. Specifically, we train a support vector machine (SVM) on log-bandpower features (7-40 Hz) derived from EEG channels after spatial filtering with a surface Laplacian, and then compute the trial-wise distance of the output of the SVM from the separating hyperplane using a cross-validation procedure. We then correlate this trial-wise performance measure, computed on EEG recordings of ten healthy subjects, with log-bandpower in the gamma frequency range (55-85 Hz), and demonstrate that it is positively correlated with frontal- and occipital gamma-power and negatively correlated with centro-parietal gamma-power. This correlation is shown to be highly significant on the group level as well as in six out of ten subjects on the single-subject level. We then utilize the framework for causal inference developed by Pearl, Spirtes and others [3,4] to present evidence that gamma-power is not only correlated with BCI performance but does indeed exert a causal influence on it. Discussion and Conclusions Our results indicate that successful execution of motor imagery, and hence reliable communication by means of a BCI based on motor imagery, requires a volitional shift of gamma-power from centro-parietal to frontal and occipital regions. As such, our results provide the first non-trivial explanation for the variation in BCI performance across and within subjects. As this topographical alteration in gamma-power is likely to correspond to a specific attentional shift, we propose to provide subjects with feedback on their topographical distribution of gamma-power in order to establish the attentional state required for successful execution of motor imagery.

Author(s): Grosse-Wentrup, M. and Hill, J. and Schölkopf, B.
Year: 2010
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Event Name: 4th International BCI Meeting 20120
Event Place: Asilomar, CA, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Causal Influence of Gamma Oscillations on Performance in Brain-Computer Interfaces},
  author = {Grosse-Wentrup, M. and Hill, J. and Sch{\"o}lkopf, B.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2010},
  month_numeric = {6}