Listening to preferred music (that which is chosen by the participant) has been shown to be effective in mitigating the effects of pain when compared to silence and a variety of distraction techniques. The wide range of genre, tempo, and structure in music chosen by participants in studies utilizing experimentally induced pain has led to the assertion that structure does not play a significant role, rather listening to preferred music renders the music "functionally equivalent" as regards its effect upon pain perception. This study addresses this assumption and performs detailed analysis of a selection of music chosen from three pain studies. Music analysis showed significant correlation between timbral and tonal aspects of music and measurements of pain tolerance and perceived pain intensity. Mood classification was performed using a hierarchical Gaussian Mixture Model, which indicated the majority of the chosen music expressed contentment. The results suggest that in addition to personal preference, associations with music and the listening context, emotion expressed by music, as defined by its acoustical content, is important to enhancing emotional engagement with music and therefore enhances the level of pain reduction and tolerance.

Background and Aim: Drumming requires excellent motor control and temporal coordination. Deploying specific muscle activation patterns may help achieve these requirements. Muscle activation patterns that involve reciprocal contraction of antagonist muscles are particularly favorable as they enable a high level of muscular economy while maintaining performance. In contrast, simultaneous contraction of antagonist muscles is an inefficient muscle activation pattern. In drumming, co-contraction can lead to increased movement variability and greater fatigue over time. In this study we examine how muscle activation patterns develop with increased drumming expertise. Methods: Eleven expert drummers (ED) and eleven amateur drummers (AD) were recorded using 3D motion capture while performing five different uni-manual and bi-manual repetitive drumming tasks across different tempi. Electromyography was used to record muscle activation of wrist flexor and extensor muscles. Results: Findings indicate that reduced co-contraction resulted in more even drumming performance. Co-contraction also increased in extremely slow and very high tempi. Furthermore, regardless of task or tempo, muscle co-contraction was decreased in participants with higher levels of expertise. In addition to anti-phasic activity of wrist flexor and extensor muscles, expert drummers exhibited a flexor dominance, suggesting more efficient usage of rebound. Conclusion: Taken together, we found that higher levels of drumming expertise go hand in hand with specific muscle activation patterns that can be linked to more precise and efficient drumming performance.

The 6-h IDEX SPECT scan is a viable alternative to FDG PET imaging in seizure onset localisation in TLE.

Background: High-speed drumming requires precise control over the timing, velocity, and magnitude of striking movements. Aim: To examine effects of tempo and expertise on unaccented repetitive drumming performance using 3D motion capture. Methods: Expert and amateur drummers performed unimanual, unaccented, repetitive drum strikes, using their dominant right hand, at five different tempi. Performance was examined with regard to timing variability, striking velocity variability, the ability to match the prescribed tempo, and additional variables. Results: Permutated multivariate analysis of variance (PERMANOVA) revealed significant main effects of tempo (p < .001) and expertise (p <.001) on timing variability and striking velocity variability; low timing variability and low striking velocity variability were associated with low/medium tempo as well as with increased expertise. Individually, improved precision appeared across an optimum tempo range. Precision was poorest at maximum tempo (400 hits per minute) for precision variables. Conclusions: Expert drummers demonstrated greater precision and consistency than amateurs. Findings indicate an optimum tempo range that extends with increased expertise.

The voice plays a crucial role in expressing emotion in popular music. However, the importance of the voice in this context has not been systematically assessed. This study investigates the emotional effect of vocal features in popular music. In particular, it focuses on nonverbal characteristics, including vocal melody and rhythm. To determine the efficacy of these features, they are used to construct a computational Music Emotion Recognition (MER) system. The system is based on the circumplex model that expresses emotion in terms of arousal and valence. Two independent studies were used to develop the system. The first study established models for predicting arousal and valence based on a range of acoustical and nonverbal vocal features. The second study was used for independent validation of these models. Results show that features describing rhythmic qualities of the vocal line produce emotion models with a high level of generalizability. In particular these models reliably predict emotional valence, a well-known issue in existing Music Emotion Recognition systems.