Meditation has been shown to improve mental health by reducing stress and enhancing cognitive functions. To monitor meditation quality, Electroencephalogram (EEG) offers an objective alternative to self-evaluation questionnaires, which suffer from subjectivity and delays. However, previous EEG protocols often inflated classification performance due to temporal correlation and auditory interference. This study introduces a new data acquisition protocol to address these issues. We constructed a dataset from 32 subjects, each participating on three sessions in different days to eliminate temporal interference. During each session, subjects alternated between audio-guided meditation and silent unguided meditation. Using the Multi-view Spectral-Spatial-Temporal Masked Autoencoder (MV-SSTMA) model which was pre-trained on the Emotion EEG dataset SEED, the model achieved superior classification performance in F1 score, accuracy, precision, and recall. These findings offer valuable insights for improving EEG-based meditation monitoring in biomedical applications.

Sensitivity analysis determines how a system responds to stimuli variations, which can benefit important software-engineering tasks such as change-impact analysis. We present SENSA, a novel dynamic-analysis technique and tool that combines sensitivity analysis and execution differencing to estimate the dependencies among statements that occur in practice. In addition to identifying dependencies, SENSA quantifies them to estimate how much or how likely a statement depends on another. Quantifying dependencies helps developers prioritize and focus their inspection of code relationships. To assess the benefits of quantifying dependencies with SENSA, we applied it to various statements across Java subjects to find and prioritize the potential impacts of changing those statements. We found that SENSA predicts the actual impacts of changes to those statements more accurately than static and dynamic forward slicing. Our SENSA prototype tool is freely available for download.

Program slicing is a popular but imprecise technique for identifying which parts of a program affect or are affected by a particular value. A major reason for this imprecision is that slicing reports all program statements possibly affected by a value, regardless of how relevant to that value they really are. In this paper, we introduce quantitative slicing (q-slicing), a novel approach that quantifies the relevance of each statement in a slice. Q-slicing helps users and tools focus their attention first on the parts of slices that matter the most. We present two methods for quantifying slices and we show the promise of q-slicing for a particular application: predicting the impacts of changes.