Search

Background: Commonly used models for eliciting muscle pain involve the injection of algesic substances or the induction of delayed onset muscle soreness. The former require invasive procedures, and the time frame for pain induction and subsidence in the latter can be inconvenient. This study presents a detailed spatiotemporal characterization of a new experimental model of muscle pain based on short‐wave diathermy (SWD), developed to overcome the limitations of existing models. Methods: The shoulder was selected as target site and the effects of the model were tested in two sessions to assess its reliability. Pain intensity profiles were recorded during the application of SWD, and changes in pressure pain threshold (PPT) in the infraspinatus muscle, together with pain intensity, duration, and quality were assessed 30 min after induction. Results: SWD‐induced pain intensity scores averaged 4 points on a visual analogue scale, whereas PPT showed a consistent decrease of about 25% relative to baseline values. Pain was localized in the shoulder area, and was described as continuous, dull, well‐delimited, heavy, and bearable. Pain lasted for an average of 145 min without requiring reinduction and was reliably elicited in both experimental sessions. Conclusion: SWD can be used to elicit experimental muscle pain in a non‐invasive, long‐lasting, and reliable way and allows for repeated within‐ and between‐session testing in the shoulder. Significance Statement: SWD produces deep heating in muscles by converting electromagnetic energy to thermal energy. It was previously shown that it can be used to elicit experimental pain in the forearm muscles, and the present study demonstrates that this can be reliably generalized to other body sites, such as the shoulder. Furthermore, SWD application is non‐invasive and presents a convenient time frame for pain induction and subsidence, thus overcoming limitations associated with traditional muscle pain models. [ABSTRACT FROM AUTHOR]

The use of Bayes factors is becoming increasingly common in psychological sciences. Thus, it is important that researchers understand the logic behind the Bayes factor in order to correctly interpret it, and the strengths of weaknesses of the Bayesian approach. As education for psychological scientists focuses on frequentist statistics, resources are needed for researchers and students who want to learn more about this alternative approach. The aim of the current article is to provide such an overview to a psychological researcher. We cover the general logic behind Bayesian statistics, explain how the Bayes factor is calculated, how to set the priors in popular software packages to reflect the prior beliefs of the researcher, and finally provide a set of recommendations and caveats for interpreting Bayes factors. The current article discusses two statistical approaches that are commonly used in psychological sciences: a Bayesian approach and a frequentist approach. While a frequentist approach is generally taught to aspiring psychology researchers, the Bayesian approach is becoming increasingly popular. Here, we provide an introduction and tutorial on using the Bayesian approach to test hypotheses. We cover the general logic behind Bayesian statistics, explain how the Bayes factor is calculated and the settings in popular software packages, and finally provide a set of recommendations and caveats for interpreting Bayes factors. [ABSTRACT FROM AUTHOR]

Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single‐trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state‐of‐the‐art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four‐class classification) from pre‐movement single‐trial EEG (100 ms and up to 1,600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single‐trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation. [ABSTRACT FROM AUTHOR]