Usability of an augmented reality bedtime routine application for autistic children.

Sleep problems are highly prevalent in autism and negatively impact the physical and mental health of children and their caregivers. Sleep education programs are often recommended as a first line-treatment to help parents implement healthy sleeping habits and a bedtime routine at home; however, the accompanying paper-based toolkits used in the bedtime routines have limitations related to engagement and adherence. To address these gaps, we iteratively developed and tested the usability of an augmented reality (AR) bedtime routine application. Our single participant design study (n = 7 child/parent dyads) found 86% compliance with the program and suggested good-excellent usability of the app with a trend toward increased willingness and faster completion of children's bedtime routines. This work supports the feasibility of using technology-based tools in sleep education programs and informs future clinical studies examining the effectiveness of these approaches for mitigating sleep difficulties.

How capable is non-invasive EEG data of predicting the next movement? A mini review.

In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users.

Constrained blind source extraction of readiness potentials from EEG.

One of the changes seen in electroencephalography (EEG) data preceding human voluntary movement is a cortical potential called readiness potential (RP). Detection of this potential can benefit researchers in clinical neurosciences for rehabilitation of malfunctioning brain and those working on brain-computer interfacing to develop a suitable mechanism to detect the intention of movement. Here, a constrained blind source extraction (CBSE) is attempted for detection of RP. A suitable constraint is defined and applied. The results are also compared with those of the traditional blind source separation in terms of true positive rate, false positive rate, and computation time. The results show that the CBSE approach in overall has superior performance.