Hybrid in-phase and continuous auditory stimulation significantly enhances slow wave activity during sleep.

Recent evidence has shown that enhancing slow-wave activity (SWA) during sleep has positive effects on cognitive, metabolic, and autonomic function. We have developed a consumer, integrated device that automatically detects sleep stages from a single electroencephalogram (EEG) signal and delivers auditory stimulation in a closed-loop manner. The stimulation was delivered in 15-auditory tone blocks separated from each other by at least 15 seconds.

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.

We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance. We evaluated 58 different architectures and training configurations using three-fold cross validation.

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM.