TY - JOUR
AU - 
AB - METHODS published: 25 April 2016 doi: 10.3389/fnhum.2016.00170 Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task 1 1 2 1 Andreas Meinel , Sebastián Castaño-Candamil , Janine Reis and Michael Tangermann * Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany, Department of Neurology, Albert-Ludwigs-University, Freiburg, Germany 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 Edited by: Klaus Gramann, and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG Berlin Institute of Technology, data of a short time interval prior to 
TI - Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task
JF - Frontiers in Human Neuroscience
DO - 10.3389/fnhum.2016.00170
DA - 2016-04-25
UR - https://www.deepdyve.com/lp/unpaywall/pre-trial-eeg-based-single-trial-motor-performance-prediction-to-BAvijmPsB0
DP - DeepDyve
ER -