Investigation into the effects of music and meditation on galvanic skin response

Brain Melody Interaction: Understanding Effects of Music on Cerebral Hemodynamic Responses

Music elicits strong emotional reactions in people, regardless of their gender, age or cultural background. Understanding the effects of music on brain activity can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research. We explore the effects of three different music genres on people’s cerebral hemodynamic responses. Functional near-infrared spectroscopy (fNIRS) signals were collected from 27 participants while they listened to 12 different pieces of music. The signals were pre-processed to reflect oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentrations in the brain. K-nearest neighbor (KNN), random forest (RF) and a one-dimensional (1D) convolutional neural network (CNN) were used to classify the signals using music genre and subjective responses provided by the participants as labels. Results from this study show that the highest accuracy in distinguishing three music genres was achieved by deep learning models (73.4% accuracy in music genre classification and 80.5% accuracy when predicting participants’ subjective rating of emotional content of music). This study validates a strong motivation for using fNIRS signals to detect people’s emotional state while listening to music. It could also be beneficial in giving personalised music recommendations based on people’s brain activity to improve their emotional well-being.

Culture and mental health resilience in times of COVID-19

This paper aims to clarify the role of culture as a public good that serves to preserve mental health. It tests the evolutionary hypothesis that cultural consumption triggers a microeconomic mechanism for the self-defense of mental health from uncertainty. The COVID-19 pandemic offers a natural experiment of cultural consumption under increased uncertainty. Using primary data from a pilot survey conducted online during the pandemic and applying Probit and Heckman selection models, the study analyzes levels of happiness and propensity to help others. The results suggest that past consumption of culture is associated with higher happiness levels during crises. Moreover, spontaneous cultural practices (such as group singing) during times of uncertainty are associated with an increase in the pro-social propensity to help others. These findings highlight culture as a tool for promoting mental health at the micro level and social capital resilience at the aggregate level.

The impact of virtual reality meditation on college students’ exam performance

Advocates of meditation claim that it can improve various aspects of life, including health, attention, thinking, and learning. The purpose of this empirical, quantitative, between-subject study was twofold. First, it compared the effectiveness of meditation delivered through virtual reality versus video, as measured by students’ test scores. Second, the study provided insights on the use of meditation, whether via virtual reality or video, as a way to positively affect well-being. T-test analysis showed virtual reality meditation to be significantly more beneficial than video meditation. Students reported that meditation techniques delivered using either medium to be helpful in decreasing their pre-exam anxiety. This study has practical implications and offers evidence on the beneficial impact of VR meditation on students’ exam performance and anxiety levels.

The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features

Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.

Assessing the potential of electrodermal activity as an alternative access pathway

An embedded multiple-case study was conducted with six able-bodied participants to evaluate the potential of electrodermal activity (EDA) as an alternative access pathway to electronic aids to daily living. Electrodermal signals were recorded while participants alternated between rest and three different mental or breathing exercises. In a subsequent experimental session, the exercise exerting the greatest influence on EDA was used to volitionally generate an 'active' state. Two classification algorithms, namely, a probabilistic classifier and a handcrafted rule base were developed and tailored to each individual's physiological patterns to discriminate between participant states. Through cross-validation, participant state was correctly identified to an accuracy exceeding 80% using either classification algorithm. This result demonstrates that consciously controlled EDA could conceivably serve as a binary switch, and encourages further research towards EDA-based alternative access for people who are locked-in.

Conscious control of electrodermal activity: the potential of mental exercises

Few practical methods of communicating with people who are locked-in currently exit. This study investigates conscious control of physiological signals, specifically electrodermal activity, to generate two distinguishable and reproducible states. These states can be translated into a binary signal to control a communication device for people who are locked in. Breathing rates, mental arithmetic and mental music were investigated as means of controlling electrodermal activity. Features extracted from the signals included mean, range and number of electrodermal reactions. For these of four subjects, at least one mental technique caused a reproducible, statistically significant the potential of using mental exercises to develop volitional control of electrodermal activity.