Addressing Temporal and Auditory Factors in Meditative EEG with Self-Supervised Learning

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.