title: Multiscale temporal neural dynamics predict performance in a complex sensorimotor task.
abstract: Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.
authors.affiliation: Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
authors.forename: Wojciech
authors.initials: W
authors.lastname: Samek
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Duncan A J
authors.initials: DAJ
authors.lastname: Blythe
authors.affiliation: Neurophysics Group, Department of Neurology, Charité University Medicine, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Curio
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.affiliation: Neurotechnology Group, Berlin Institute of Technology, Berlin, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
authors.affiliation: Neurophysics Group, Department of Neurology, Charité University Medicine, Berlin, Germany; Center for Cognition and Decision Making, National Research University Higher School of Economics, Russian Federation. Electronic address: vadim.nikulin@charite.de.
authors.forename: Vadim V
authors.initials: VV
authors.lastname: Nikulin
mesh_terms: D000328:Adult; D000513:Alpha Rhythm / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009068:Movement / Q000502:physiology*; D011379:Prognosis; D011597:Psychomotor Performance / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology*