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Meinel A, Castaño-Candamil S, Reis J, et al. Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Front Hum Neurosci. 2016;10:170doi: 10.3389/fnhum.2016.00170.
Meinel, A., Castaño-Candamil, S., Reis, J., & Tangermann, M. (2016). Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Frontiers in human neuroscience, 10170. https://doi.org/10.3389/fnhum.2016.00170
Meinel, Andreas, et al. "Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task." Frontiers in human neuroscience vol. 10 (2016): 170. doi: https://doi.org/10.3389/fnhum.2016.00170
Meinel A, Castaño-Candamil S, Reis J, Tangermann M. Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Front Hum Neurosci. 2016 Apr 25;10:170. doi: 10.3389/fnhum.2016.00170. eCollection 2016. PMID: 27199701; PMCID: PMC4843706.
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Front Hum Neurosci. 2016 Apr 25;10:170. doi: 10.3389/fnhum.2016.00170. eCollection 2016.

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task.

Frontiers in human neuroscience

Andreas Meinel, Sebastián Castaño-Candamil, Janine Reis, Michael Tangermann

Affiliations

  1. Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany.
  2. Department of Neurology, Albert-Ludwigs-University Freiburg, Germany.

PMID: 27199701 PMCID: PMC4843706 DOI: 10.3389/fnhum.2016.00170

Abstract

We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

Keywords: EEG; hand motor rehabilitation; isometric force modulation; oscillatory subspace; single-trial performance prediction; spatial filtering; trial-by-trial variability; visuomotor integration

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