Mario Chávez

This study investigates brain-heart interactions during systemic anoxia and finds that coupling between cardiac parasympathetic activity and high-frequency brain signals predicts survival after resuscitation.

This study shows that motor imagery modulates cardiac-cortical and cerebellar networks, linking brain-heart interactions to motor learning and performance.

This paper identifies neuronal avalanches as biomarkers to predict BCI performance and guide personalized training protocols.

The paper investigates cardiac-cerebrovascular interactions in elderly individuals, finding that constrained ventilation and impaired cerebrovascular control are associated with elevated cardiac sympathetic activity.

This paper introduces a robust estimator for time-resolved cardiac sympathetic and parasympathetic indices using HR and HRV dynamics, outperforming standard spectral methods.

This paper introduces Cycle-Frequency (C-F) analysis to improve the assessment of respiratory-related cortical activity by enhancing time and frequency localization compared to conventional Time-Frequency analysis.

This study develops a method to estimate time-resolved sympathetic and parasympathetic modulations in rat cardiac dynamics, validated against standard LF and HF measures.

This study investigates alpha activity neuromodulation through neurofeedback training in a double-blind randomized study, finding increased alpha activity only in the neurofeedback group.

This study uses intracranial EEG with direct brain stimulation to map natural frequencies in the human brain, revealing diverse spectral patterns at a local scale.

This study shows that increased task-related activity and reduced connectivity between non-targeted brain regions predict better performance during BCI learning.