Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

Objective: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.

Methods: A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites.

Gut-Brain Computer Interfacing (GBCI) : Wearable Monitoring of Gastric Myoelectric Activity.

We propose a new area for wearable technology and interaction by acquiring gastrointestinal signals non-invasively from the abdomen. The mind-gut connection has flourished as a research area in the past two decades, elucidating the guts key role in stress, affect, and memory. Meanwhile, engineering advancements have shown potential in accuracy of non-invasive gastric recordings.

Dopamine signaling tunes spatial pattern selectivity in C. elegans.

Animals with complex brains can discriminate the spatial arrangement of physical features in the environment. It is unknown whether such sensitivity to spatial patterns can be accomplished in simpler nervous that lack long-range sensory modalities such as vision and hearing. Here we show that the nematode can discriminate spatial patterns in its surroundings, despite having a nervous system of only 302 neurons.

A sorting strategy for C. elegans based on size-dependent motility and electrotaxis in a micro-structured channel.

Caenorhabditis elegans (C. elegans) is a model organism widely utilized in various fundamental studies in developmental, neural and behavioural biology. The worm features four distinct larval stages, and many research questions are stage-specific; therefore, it is necessary to sort worms by their developmental stages, which are typically represented by different size ranges.