covariance (ANCOVA) on a per-protocol sample, there were no differences between MBCR and SET groups on T/S ratios, but a trend effect was observed between the combined intervention group and controls (p = .054) whereby TL in the intervention group was maintained, whereas TL decreased for control participants. Similarly cortisol slopes in both intervention groups were maintained over time but became flatter in control participants (p < .05). Stress scores also improved significantly over time in the MBCR group. Conclusion: Psychosocial interventions providing stress reduction and emotional support resulted in TL and cortisol slope maintenance in distressed breast cancer survivors, compared to decreases in usual care. MBCR participants improved the most on psychosocial outcomes. Implications of this finding require further exploration.Purpose: Mindfulness meditation (MM) has increasing evidence of benefit for a variety of health conditions. EEG changes have been noted short-term during a meditation session as well as long-term from continued practice. Most studies examine EEG changes alone and do not include other physiological measures. The purpose of this study was to analyze EEG and respiration changes during meditation using advanced signal processing techniques and machine learning. Methods: EEG and respiration data were collected and analyzed from novice meditators after a 6-week one-on-one MM intervention previously reported on (Wahbeh et al., 2012). The meditation was a guided mindfulness of breath meditation delivered with an audio recording; no specific instructions to slow breathing were given. Research participants were relatively healthy adults aged 50-75 years with Perceived Stress Scale > 8. Collected data were analyzed with spectral analysis using a Stockwell transform, synchrony using phase locked value, and support vector machine (SVM) classifier to evaluate an objective marker for meditation. Results: Data are reported from 34 participants (mean age 61 years). There was a higher power and greater synchrony in alpha, theta and beta bands during meditation. There was slower respiration frequency during meditation. Using EEG or respiration signals individually in the SVM, the best classifier averaged across participants was 78% and 76% respectively but using an SVM classifier that included both signals, the best classifier across participants was 85% (ANOVA using the three within subject accuracies, p < .001).) Conclusion: Similar to other studies, we observed increased power in theta and alpha power during meditation and additionally found increased beta power which has been less consistently observed. While individually the EEG and respiration signals helped a classifier differentiate recordings during meditation from control recordings, a classifier using EEG and respiration signals together had a higher discrimination accuracy than one using the EEG or respiration signal alone.Purpose: Diminished control of standing posture, as indicated by traditional measures of postural sway, including increa...