In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization ($\texttt{CMMN}$), which consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data.

In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization ( \texttt{CMMN}), which consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that \texttt{CMMN} leads to significant and consistent performance gains independent from the neural network architecture when adapting between subjects, sessions, and even datasets collected with different hardware. Notably our performance gain is on par with much more numerically intensive Domain Adaptation (DA) methods and can be used in conjunction with those for even better performances.

The newest version of Google's Nest Hub promises better sound – and better sleep. But consumers having trouble sleeping may be more drawn to its new built-in Sleep Sensing technology that uses Google's Soli motion detection chip – first used in the Pixel 4 smartphone – to know how long you slept. The Soli-driven radar does not identify specific bodies or faces, but will watch your movements and breathing, as well as track when you snore or cough to build a pattern of your sleep – and what could be interrupting it. Facebook's vaccine finder:The new tool can help you book a COVID-19 vaccine appointment Google's consumer research found about 20% of Nest Hub displays owned by consumers are in bedrooms. "When we talked to people about what they most wanted help with, they resoundingly responded with'Can you help me get better quality of sleep?' That's what people were really interested in," Ashton Udall, senior Nest product manager, told USA TODAY.

MIT researchers have developed a wireless, private way to monitor a person's sleep postures -- whether snoozing on their back, stomach, or sides -- using reflected radio signals from a small device mounted on a bedroom wall. The device, called BodyCompass, is the first home-ready, radio-frequency-based system to provide accurate sleep data without cameras or sensors attached to the body, according to Shichao Yue, who will introduce the system in a presentation at the UbiComp 2020 conference on Sept. 15. The PhD student has used wireless sensing to study sleep stages and insomnia for several years. "We thought sleep posture could be another impactful application of our system" for medical monitoring, says Yue, who worked on the project under the supervision of Professor Dina Katabi in the MIT Computer Science and Artificial Intelligence Laboratory. Studies show that stomach sleeping increases the risk of sudden death in people with epilepsy, he notes, and sleep posture could also be used to measure the progression of Parkinson's disease as the condition robs a person of the ability to turn over in bed.

In this work we investigate intra-day patterns of activity on a population of 7,261 users of mobile health wearable devices and apps. We show that: (1) using intra-day step and sleep data recorded from passive trackers significantly improves classification performance on self-reported chronic conditions related to mental health and nervous system disorders, (2) Convolutional Neural Networks achieve top classification performance vs. baseline models when trained directly on multivariate time series of activity data, and (3) jointly predicting all condition classes via multi-task learning can be leveraged to extract features that generalize across data sets and achieve the highest classification performance.