A brief 6-week breathing-based mindfulness intervention reduced anxiety and stress levels in undergraduate students with high neuroticism, with p-values of 0.013 and 0.027, respectively. Emotion regulation strategy use, specifically suppression, also changed significantly. Electroencephalogram (EEG) analysis revealed differences before and after the intervention in resting states and tasks, with Fp1 and O2 channels being most significant. Machine learning algorithms classified before and after conditions with accuracy around 77%, sensitivity 76–80%, specificity 73–77%, and AUC 0.66–0.8. Mindfulness can thus improve self-regulation of emotional state in neurotic students based on psychometric and electrophysiological analyses.
Classifying meditation versus non-meditation from EEG data across different people and multiple sessions is feasible but less accurate than classifying within a single person. Twelve participants each completed five sessions of loving-kindness meditation (directed toward self or others) and non-meditation. Machine learning algorithms trained on session data from a common pool achieved mean accuracies up to 62.3% for distinguishing self-directed meditation from non-meditation, compared to 72.1% in previous within-subject work. Combining two feature extraction methods (common spatial patterns and short-time Fourier transform) outperformed either method alone in 83.3% of test instances, and using more training sessions improved accuracy in 75.0% of instances. Results varied widely depending on which sessions were selected for training and testing.
A machine-learning model using resting-state electroencephalogram (EEG) signals can assess alexithymia severity with moderate accuracy. Functional connectivity in brain regions related to the default mode network, estimated via source localization, served as explanatory variables. The model classified individuals as having low or high alexithymia severity, achieving a maximum ROC-AUC score of 0.70. Analysis using explainable artificial intelligence revealed that functional connectivity in the theta and gamma frequency bands, particularly in the Left Hippocampus, was most effective for assessment. The findings suggest EEG and machine learning offer a potential objective method for evaluating alexithymia, a risk factor for several mental disorders.
Over six weeks, distributing sprint training in smaller, more frequent doses (microdosing) was compared to traditional twice-weekly sessions in twenty male professional field hockey players. Both groups completed the same total sprint volume. Sprint performance and horizontal force-velocity profiles were measured before and after the intervention. No significant differences in sprint times or mechanical variables were found between the groups after training. However, within the microdosing group, significant improvements occurred in maximal theoretical horizontal force, maximal power, and sprint times from 5 to 25 meters, driven by increased stride length and decreased stride frequency at maximal velocity. Microdosing training load appears an effective and efficient sprint training method for team sports.