2019
Antle, Alissa N., McLaren, Elgin-Skye, Fiedler, Holly, Johnson, Naomi
Evaluating the Impact of a Mobile Neurofeedback App for Young Children at School and Home Proceedings Article
In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–13, Association for Computing Machinery, Glasgow, Scotland Uk, 2019, ISBN: 9781450359702.
Abstract | Links | BibTeX | Tags: brain computer interfaces, children, field studies, hci for mental health, mindfull, positive computing, self-regulation
@inproceedings{10.1145/3290605.3300266,
title = {Evaluating the Impact of a Mobile Neurofeedback App for Young Children at School and Home},
author = {Alissa N. Antle and Elgin-Skye McLaren and Holly Fiedler and Naomi Johnson},
url = {https://doi.org/10.1145/3290605.3300266},
doi = {10.1145/3290605.3300266},
isbn = {9781450359702},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
pages = {1–13},
publisher = {Association for Computing Machinery},
address = {Glasgow, Scotland Uk},
series = {CHI '19},
abstract = {About 18% of children in industrialized countries suffer from anxiety. We designed a mobile neurofeedback app, called Mind-Full, based on existing design guidelines. Our goal was for young children in lower socio-economic status schools to improve their ability to self-regulate anxiety by using Mind-Full. In this paper we report on quantitative outcomes from a sixteen-week field evaluation with 20 young children (aged 5 to 8). Our methodological contribution includes using a control group, validated measures of anxiety and stress, and assessing transfer and maintenance. Results from teacher and parent behavioral surveys indicated gains in children's ability to self-regulate anxiety at school and home; a decrease in anxious behaviors at home; and cortisol tests showed variable improvement in physiological stress levels. We contribute to HCI for mental health with evidence that it is viable to use a mobile app in lower socio-economic status schools to improve children's mental health.},
keywords = {brain computer interfaces, children, field studies, hci for mental health, mindfull, positive computing, self-regulation},
pubstate = {published},
tppubtype = {inproceedings}
}
About 18% of children in industrialized countries suffer from anxiety. We designed a mobile neurofeedback app, called Mind-Full, based on existing design guidelines. Our goal was for young children in lower socio-economic status schools to improve their ability to self-regulate anxiety by using Mind-Full. In this paper we report on quantitative outcomes from a sixteen-week field evaluation with 20 young children (aged 5 to 8). Our methodological contribution includes using a control group, validated measures of anxiety and stress, and assessing transfer and maintenance. Results from teacher and parent behavioral surveys indicated gains in children's ability to self-regulate anxiety at school and home; a decrease in anxious behaviors at home; and cortisol tests showed variable improvement in physiological stress levels. We contribute to HCI for mental health with evidence that it is viable to use a mobile app in lower socio-economic status schools to improve children's mental health.
2018
Antle, Alissa N., Chesick, Leslie, Mclaren, Elgin-Skye
Opening up the Design Space of Neurofeedback Brain--Computer Interfaces for Children Journal Article
In: ACM Trans. Comput.-Hum. Interact., vol. 24, no. 6, 2018, ISSN: 1073-0516.
Abstract | Links | BibTeX | Tags: Brain-computer interfaces, children, conceptual framework, design, mental health, mindfull, self-regulation, strong concepts
@article{10.1145/3131607,
title = {Opening up the Design Space of Neurofeedback Brain--Computer Interfaces for Children},
author = {Alissa N. Antle and Leslie Chesick and Elgin-Skye Mclaren},
url = {https://doi.org/10.1145/3131607},
doi = {10.1145/3131607},
issn = {1073-0516},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {ACM Trans. Comput.-Hum. Interact.},
volume = {24},
number = {6},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Brain--computer interface applications (BCIs) utilizing neurofeedback (NF) can make invisible brain states visible in real time. Learning to recognize, modify, and regulate brain states is critical to all children's development and can improve learning, and emotional and mental health outcomes. How can we design usable and effective NF BCIs that help children learn and practice brain state self-regulation? Our contribution is a list of challenges for this emerging design space and a conceptual framework that addresses those challenges. The framework is composed of five interrelated strong concepts that we adapted from other design spaces. We derived the concepts reflectively, theoretically, and empirically through a design research process in which we created and evaluated a NF BCI, called Mind-Full, designed to help children living in Nepal who had suffered from complex trauma learn to self-regulate anxiety and attention. We add rigor to our derivation methodology by horizontally and vertically grounding our concepts, that is, relating them to similar concepts in the literature and instantiations in other artifacts. We illustrate the generative power of the concepts and the inter-relationships between them through the description of two new NF BCIs we created using the framework for urban and indigenous children with anxiety and attentional challenges. We then show the versatility of our framework by describing how it inspired and informed the conceptual design of three NF BCIs for different types of self-regulation: selective attention and working memory, pain management, and depression. Last, we discuss the contestability, defensibility, and substantiveness of our conceptual framework in order to ensure rigor in our research design process. Our contribution is a rigorously derived design framework that opens up this new and emerging design space of NF BCI's for children for other researchers and designers.},
keywords = {Brain-computer interfaces, children, conceptual framework, design, mental health, mindfull, self-regulation, strong concepts},
pubstate = {published},
tppubtype = {article}
}
Brain--computer interface applications (BCIs) utilizing neurofeedback (NF) can make invisible brain states visible in real time. Learning to recognize, modify, and regulate brain states is critical to all children's development and can improve learning, and emotional and mental health outcomes. How can we design usable and effective NF BCIs that help children learn and practice brain state self-regulation? Our contribution is a list of challenges for this emerging design space and a conceptual framework that addresses those challenges. The framework is composed of five interrelated strong concepts that we adapted from other design spaces. We derived the concepts reflectively, theoretically, and empirically through a design research process in which we created and evaluated a NF BCI, called Mind-Full, designed to help children living in Nepal who had suffered from complex trauma learn to self-regulate anxiety and attention. We add rigor to our derivation methodology by horizontally and vertically grounding our concepts, that is, relating them to similar concepts in the literature and instantiations in other artifacts. We illustrate the generative power of the concepts and the inter-relationships between them through the description of two new NF BCIs we created using the framework for urban and indigenous children with anxiety and attentional challenges. We then show the versatility of our framework by describing how it inspired and informed the conceptual design of three NF BCIs for different types of self-regulation: selective attention and working memory, pain management, and depression. Last, we discuss the contestability, defensibility, and substantiveness of our conceptual framework in order to ensure rigor in our research design process. Our contribution is a rigorously derived design framework that opens up this new and emerging design space of NF BCI's for children for other researchers and designers.
2015
Antle, Alissa N., Chesick, Leslie, Levisohn, Aaron, Sridharan, Srilekha Kirshnamachari, Tan, Perry
Using Neurofeedback to Teach Self-Regulation to Children Living in Poverty Proceedings Article
In: Proceedings of the 14th International Conference on Interaction Design and Children, pp. 119–128, Association for Computing Machinery, Boston, Massachusetts, 2015, ISBN: 9781450335904.
Abstract | Links | BibTeX | Tags: brain computer interfaces, children, developing countries, evaluation, games for learning, mindfull, neurofeedback, self-regulation
@inproceedings{10.1145/2771839.2771852,
title = {Using Neurofeedback to Teach Self-Regulation to Children Living in Poverty},
author = {Alissa N. Antle and Leslie Chesick and Aaron Levisohn and Srilekha Kirshnamachari Sridharan and Perry Tan},
url = {https://doi-org.proxy.lib.sfu.ca/10.1145/2771839.2771852},
doi = {10.1145/2771839.2771852},
isbn = {9781450335904},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the 14th International Conference on Interaction Design and Children},
pages = {119–128},
publisher = {Association for Computing Machinery},
address = {Boston, Massachusetts},
series = {IDC '15},
abstract = {In this paper we describe a neuro-feedback system and applications we designed and deployed to help vulnerable children at an NGO-funded school, called Nepal House Kaski, in Pokhara, Nepal. The system, called Mind-Full, enables traumatized children to learn and practice self-regulation by playing simple, culturally appropriate games using an EEG headset connected to an interactive tablet. Children can interact with Mind-Full using body actions that may change their physiology and brain states, which are sensed by the EEG headset and used as input to the games. One of the key challenges was to build an application that the children could immediately understand how to use when they are illiterate, don't speak English and have no computer experience. We describe Mind-Full and highlight the design principles we used to meet these constraints. We report on a subset of findings from a 14-week field experiment in which we use a mixed-methods approach to determine if children improved their ability to self-regulate during gameplay as well as in the classroom, playground and in therapy sessions. Findings from quantitative and qualitative assessment measures suggest that the treatment group significantly improved their ability to calm down and focus in a variety of contexts.},
keywords = {brain computer interfaces, children, developing countries, evaluation, games for learning, mindfull, neurofeedback, self-regulation},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we describe a neuro-feedback system and applications we designed and deployed to help vulnerable children at an NGO-funded school, called Nepal House Kaski, in Pokhara, Nepal. The system, called Mind-Full, enables traumatized children to learn and practice self-regulation by playing simple, culturally appropriate games using an EEG headset connected to an interactive tablet. Children can interact with Mind-Full using body actions that may change their physiology and brain states, which are sensed by the EEG headset and used as input to the games. One of the key challenges was to build an application that the children could immediately understand how to use when they are illiterate, don't speak English and have no computer experience. We describe Mind-Full and highlight the design principles we used to meet these constraints. We report on a subset of findings from a 14-week field experiment in which we use a mixed-methods approach to determine if children improved their ability to self-regulate during gameplay as well as in the classroom, playground and in therapy sessions. Findings from quantitative and qualitative assessment measures suggest that the treatment group significantly improved their ability to calm down and focus in a variety of contexts.