One of the grand challenges in neuroscience is to understand the developing brain ‘in action and in context’ in complex natural settings. To address this challenge, it is imperative to acquire brain data from freely-behaving children to assay the variability and individuality of neural patterns across gender and age.


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.


This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here:

The DataPort Repository contains the data used primarily for generating Figure 1.


Autism spectrum disorder (ASD) is characterized by qualitative impairment in social reciprocity, and by repetitive, restricted, and stereotyped behaviors/interests. Previously considered rare, ASD is now recognized to occur in more than 1% of children. Despite continuing research advances, their pace and clinical impact have not kept up with the urgency to identify ways of determining the diagnosis at earlier ages, selecting optimal treatments, and predicting outcomes. For the most part this is due to the complexity and heterogeneity of ASD.


We conducted a randomized controlled clinical trial to evaluate the efficacy of a brain-computer interface ( BCI ) -based visual and motor feedback motor imagery therapy system on cognitive, psychological and limb movement in hemiplegic stroke patients. We recruited more than 90 patients and randomly divided them into three groups : conventional treatment group, MI group and MI group based on brain-computer interface. The data set contains the evaluation data of these three groups of patients before and after treatment.


This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.


Bionic vision systems are currently limited by indiscriminate activation of all retinal ganglion cells (RGCs) – despite the dozens of known RGC types which each encode a different visual message. Here, we use spike-triggered averaging to explore how electrical responsiveness varies across RGC types toward the goal of using this variation to create type-selective electrical stimuli. A battery of visual stimuli and a randomly distributed sequence of electrical pulses were delivered to healthy and degenerating (4-week-old rd10) mouse retinas.


The concept of wellness, as proposed by Halbert L. Dunn, recognizes the importance of multiple dimensions, such as social and mental well-being, in maintaining overall health. Neglecting these dimensions can have long-term negative consequences on an individual's mental well-being. In the context of traditional in-person therapy sessions, efforts are made to manually identify underlying factors that contribute to mental disturbances, as these factors, if triggered, can potentially lead to severe mental health disorders.


This dataset collects the responses elicited in 10 different subjects brain when imagining 10 different semantic categories of stimuli belonging to visual and auditory domain.

The responses are acquired using an electrodes cap. The 126 electrodes are placed accordin to the 10/5% system by Oostenveld.


Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.