Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series

Robinson, Mark A.; Vanrenterghem, Jos; Pataky, Todd C.

doi:10.1016/j.jelekin.2014.10.018

Cited by 101 publications

(93 citation statements)

References 21 publications

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“…These statistical tests were carried out using IBM SPSS Statistics 22 (IBM Corp., Armonk, NY, USA). EMG and kinematic time-series data were analyzed using statistical parametric mapping [23] using Matlab (R2015a) in a one-way ANOVA design for unloading condition [24]. Critical thresholds were defined by generating random fields representative of the recorded data in smoothness and amplitude and adopting a threshold value ensuring that 95% of the random fields remained within these bounds (alpha = 0.05) [24, 25].…”

Section: Methodsmentioning

confidence: 99%

“…EMG and kinematic time-series data were analyzed using statistical parametric mapping [23] using Matlab (R2015a) in a one-way ANOVA design for unloading condition [24]. Critical thresholds were defined by generating random fields representative of the recorded data in smoothness and amplitude and adopting a threshold value ensuring that 95% of the random fields remained within these bounds (alpha = 0.05) [24, 25]. Post-hoc pairwise comparisons were performed between baseline and each unloading level using a Sidak correction when the critical threshold was exceeded during the first-level comparison to retain a Type I family-wise error rate of alpha = 0.05.…”

Section: Methodsmentioning

confidence: 99%

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Preserved gait kinematics during controlled body unloading

Awai

Franz

Easthope

et al. 2017

J NeuroEngineering Rehabil

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BackgroundBody weight supported locomotor training was shown to improve walking function in neurological patients and is often performed on a treadmill. However, walking on a treadmill does not mimic natural walking for several reasons: absent self-initiation, less active retraction of leg required and altered afferent input. The superiority of overground training has been suggested in humans and was shown in rats demonstrating greater plasticity especially in descending pathways compared to treadmill training. We therefore developed a body weight support system allowing unrestricted overground walking with minimal interfering forces to train neurological patients. The present study investigated the influence of different amounts of body weight support on gait in healthy individuals.MethodsKinematic and electromyographic data of 19 healthy individuals were recorded during overground walking at different levels of body weight support (0, 10, 20, 30, 40, and 50%). Upper body inclination, lower body joint angles and multi-joint coordination as well as time-distance parameters were calculated. Continuous data were analyzed with regard to distinct changes within a gait cycle across all unloading conditions.ResultsTemporal gait parameters were most sensitive to changes in body unloading while spatial variables (step length, joint angles) showed modest responses when unloaded by as much as 50% body weight. The activation of the gastrocnemius muscle showed a gradual decrease with increasing unloading while the biceps femoris muscle showed increased activity levels at 50% unloading. These changes occurred during stance phase while swing phase activity remained unaltered.ConclusionsHealthy individuals were able to keep their walking kinematics strikingly constant even when unloaded by half of their body weight, suggesting that the weight support system permits a physiological gait pattern. However, maintaining a given walking speed using close-to-normal kinematics while being unloaded was achieved by adapting muscle activity patterns. Interestingly, the required propulsion to maintain speed was not achieved by means of increased gastrocnemius activity at push-off, but rather through elevated biceps femoris activity while retracting the leg during stance phase. It remains to be investigated to what extent neurological patients with gait disorders are able to adapt their gait pattern in response to body unloading.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Preserved gait kinematics during controlled body unloading

Awai

Franz

Easthope

et al. 2017

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…The main result of this study was that smooth, random 1D trajectories generally produce false positives in 0D analyses with a probability much higher than ↵. Even for the best case -maximum smoothness (FWHM=67.0) and one scalar trajectory -false positive rates were nearly three times greater than ↵ (p=0.145, (Lenho↵ et al, 1999;Pataky et al, 2015;Robinson et al, 2015) but to our knowledge have not been previously quantified.…”

Section: Main Implicationsmentioning

confidence: 56%

The probability of false positives in zero-dimensional analyses of one-dimensional kinematic, force and EMG trajectories

Pataky

Vanrenterghem

Robinson

2016

Journal of Biomechanics

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120

123

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A false positive is the mistake of inferring an e↵ect when none exists, and although ↵ controls the false positive (Type I error) rate in classical hypothesis testing, a given ↵ value is accurate only if the underlying model of randomness appropriately reflects experimentally observed variance. Hypotheses pertaining to one-dimensional (1D) (e.g. time-varying) biomechanical trajectories are most often tested using a traditional zero-dimensional (0D) Gaussian model of randomness, but variance in these datasets variance is clearly 1D. The purpose of this study was to determine the likelihood that analyzing smooth 1D data with a 0D model of variance will produce false positives. We first used random field theory (RFT) to predict the probability of false positives in 0D analyses. We then validated RFT predictions via numerical simulations of smooth Gaussian 1D trajectories. Results showed that, across a range of public kinematic, force and EMG datasets, the median false positive rate was 0.382 and not the assumed ↵=0.05, even for a simple two-sample t test involving N =10 trajectories per group. The median false positive rates for experiments involving three-component vector trajectories was p=0.764. This rate increased to p=0.945 for two three-component vector trajectories, and to p=0.999 for six three-component vectors. This implies that experiments involving vector trajectories have a high probability of yielding 0D statistical significance when there is, in fact, no 1D e↵ect. Either (a) explicit a priori identification of 0D metrics or (b) adoption of 1D methods can more tightly control ↵.

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“…We have separately observed similar agreement between parametric and non-parametric 1D procedures for a much greater variety of 1D Biomechanics data, including EMG time series (Robinson et al, 2015), suggesting that the choice between 0D and 1D models appears to be more important than the choice between parametric and non-parametric models.…”

Section: Parametric Vs Non-parametric Proceduresmentioning

confidence: 65%

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis

Pataky

Vanrenterghem

Robinson

2015

Journal of Biomechanics

Self Cite

244

192

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Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the nonparametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D nonparametric CIs were qualitatively identical to RFT CIs, and all were very di↵erent from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, designdependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one has explicitly specified an a priori 0D variable, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one's hypothesis explicitly or implicitly pertains to whole 1D trajectories.

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Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series

Cited by 101 publications

References 21 publications

Preserved gait kinematics during controlled body unloading

Preserved gait kinematics during controlled body unloading

The probability of false positives in zero-dimensional analyses of one-dimensional kinematic, force and EMG trajectories

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis

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