Our lab’s research focuses on experimental and computational approaches to understanding the neural correlates of consciousness in humans—specifically how sleep affects the brain. We specialize in using the state-of-the-art in quantitative approaches to develop novel statistical signal processing algorithms for the analysis of neural data, with direct applications to basic science, biomarker discovery, and medical device development.
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News and Recent Work
Transient Oscillation Dynamics
We have developed a new approach to creating individualized electroencephalogram (EEG) fingerprints of brain activity during sleep, which can be used to identify biomarkers of neurological health and disease. We first identify tens of thousands of short, spindle-like EEG waveforms called time-frequency peaks (TF-peaks) across a night of sleep. Then, we create visual summaries of brain state by characterizing the activity of TF-peaks as a function of sleep depth and also cortical timing, called SO-power and SO-phase histograms. These summaries, like fingerprints, appear to be unique to individuals yet consistent night to night, providing a highly informative new visualization technique and powerful basis for understanding neurological health and disease.
We have developed an open source toolbox and tutorial.
Sleep Apnea Dynamics: Instantaneous AHI
Obstructive sleep apnea (OSA) affects at least 10% of the population and is associated with numerous comorbidities. While OSA is a complex dynamic process, the main metric for diagnosing OSA, the apnea-hypopnea index (AHI), reduces everything down to a single number. It is not surprising that the AHI has been shown to be a poor predictor of outcome.
To address this issue, we have developed a new approach, which computes an “instantaneous AHI”, which computes the moment-to-moment probability of a respiratory event as a function of changes in body position, sleep stage, and previous respiratory event activity. This model acts as a highly individualized respiratory signature, which can accurately predict the precise timing of future events and show robust differences in populations.
We have developed an open source toolbox and a tutorial.