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

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

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.

Figures

Figure 1
Figure 1
Schematic setup of the EEG-tracked SVIPT. The subject applies force to a sensor using a pinch grasp. Force is transduced into positions of a horizontally moving cursor. EEG activity is recorded before, during and after repeated trials.
Figure 2
Figure 2
Frequency parameter configurations characterized by the frequency f0 and the corresponding band width Δf. In total, 55 configurations were used for computing SPoC filters. The omitted points (gray area) correspond to the power line frequency range.
Figure 3
Figure 3
Examples of trial-wise variations of different performance metrics over a full session, and their distributions. (A–C) are taken from data of subject S9, while (D–F) are from S13.
Figure 4
Figure 4
Contrasting the predictive outcome of all 3600 tested parameter configurations for linear regression (LinReg) and over 12,000 configurations for SPoC. In (A), the overall correlation Rall between predicted and estimated target variable values is depicted. (B) Shows the performance separability z-AUC. Gray lines indicate the median, boxes enclose the 25th to 75th percentile. The whisker length is set to two inter-quartile ranges.
Figure 5
Figure 5
Characterization of exemplary predictive SPoC features. Each component is characterized line-wise labeled by the used performance metric and the rank according to the full-session filters. (A) Power spectrum of the component applied on non-bandpass filtered full data. The frequency band where the component has been trained is marked by the dashed lines. Note that for the component of S8 a broader frequency range is visualized compared to the other examples. (B) Spatial activity pattern. (C) Filter weights visualized. (D) Scatter plot between true labels ztrue and the predicted ones zest, color coded by the fold of the chronological cross-validation. (E) To illustrate the separability of the prediction, the distribution of ztrue values has been split using the corresponding trials of the upper and lower quartiles of zest, which resulted in Qlow, est and Qhigh, est. As a reference, the extreme quartiles Qlow and Qhigh of ztrue are also given (dashed curves). In addition, the z-AUC value based on the 50th percentile split is reported.
Figure 6
Figure 6
Stressing the stability of two exemplary SPoC components for two different parameter configurations (A,B). While stepwise decreasing the SNR ratio (indicated by the red arrow), z-AUC-values (solid lines) describing the separability of the prediction are plotted together with standard deviations (dashed lines). The area under the z-AUC curve—further on called AAUCSNR—describes the stability of the component under the challenge of added noise. (C) Shows the histogram of all AAUCSNR scores evaluated for the considered parameter configurations.
Figure 7
Figure 7
Characterizing the space of SPoC components by several metrics, which describe their stability and predictive information. The SNR-challenged AAUCSNR is given as a function of the performance separability z-AUC (A), in relation to the homogeneity of the correlation sign Hfolds(B), and dependent on the overall correlation Rall(C). Red data points describe the selected SPoC components after applying thresholds (dashed red lines).
Figure 8
Figure 8
Relation between separability metric z-AUC and the overall correlation Rall for all SPoC configurations (blue dots) and the selected ones (red dots). The dashed red line indicates the threshold z-AUCmin applied to select most informative components. The red bars indicate the distribution of Rall values for the selected components.
Figure 9
Figure 9
Histograms of different parameters solely for the selected SPoC components. (A) Shows the assignment over the 18 subjects. (B) Gives the allocation over data set sizes Ne (with a lower limit of 150 trials). (C) Visualizes the distribution across frequency bands. (D) Depicts the spread of components over the five utilized motor performance metrics, while (E) shows the split according to their SPoC rank positions.
Figure 10
Figure 10
Overview over typical activity patterns resulting from the selected components, grouped in three categories: G1 consists of components with neural origin, G2 comprises artifact-related subspaces and G3 captures non-informative components. Details on their parameter configurations are given in Table 1.
Figure 11
Figure 11
Relation between SPoC rank stability and pattern homogeneity over five cross-validation folds (chronological order). (A) Stationary case: component is first-ranked across all five folds (data of subject S9, f = [9.4, 11] Hz, RT). (B) Rank switching: Two almost stable components switch rank positions between folds (S5, f = [27.5, 30.3] Hz, RT). Lines connect the corresponding topologies. (C) Intensity variation: intensity of first-ranked component decreases over time folds (S13, f = [13.6, 15.3] Hz, CPL).
Figure 12
Figure 12
Influence of the frequency band upon the rank stability. While stable at f0 = 9.4 Hz, a component develops rank instability with slight increase/decrease of its frequency band (data of subject S9).
Figure 13
Figure 13
The identification of session trends vs. single-trial variations of performance is possible by localizing a predictor's characteristic with respect to two selection criteria. The scatter plot visualizes AAUCSNR as a function of the mean correlation value across folds Rfolds for all configurations (light blue) and the selected ones only (red). Two classes of predictors can be identified: single-trial predictors showing a high Rfolds value while session-trend predictors show a very low Rfolds value.

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