Exploring and Evaluating Sound for Helping Children Self-Regulate with a Brain-Computer Application
Abstract
Children in North America are more likely to suffer from attentional challenges than any other mental health issue. Studies suggest that neurofeedback treatments may be useful for helping these children learn to self-regulate. Applying neurofeedback treatments in real-world, school settings poses a challenge, however, as these environments are often noisy and filled with distractions. The addition of ambient audio to neurofeedback systems may help reduce these disruptions. Further, research suggests that certain auditory treatments, such as binaural beats and white noise, may improve children's focus and aid memory recall. In the following paper we present the theories supporting this idea as well as a mixed methods framework for evaluating whether sound can help children focus while learning to self-regulate using a neurofeedback system. Specifically, we wish to investigate whether these treatments may help children (1) achieve an attentive state sooner and (2) maintain an attentive state for longer, when compared to the same system without sound.
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School settings pose a challenge to children with ADHD because these environments are often noisy and filled with distractions.
“Some researchers have noted the impact of context contention, for example how school settings pose a challenge to children with ADHD, whilst 'these environments are often noisy and filled with distractions' (McLaren and Antle, 2017).”
From Intensity and Variable Attention: Counter Narrating ADHD, from ADHD Deficits to ADHD Difference (2023) · cites this paper as McLaren and Antle, 2017
Cites
The self-regulating brain and neurofeedback: experimental science and clinical promise
Thibault, R. T., Lifshitz, M., & Raz, A. (2016) · Cortex · doi:10.1016/j.cortex.2015.10.024 · Cited by 272 · see citing works
Listen to the noise: noise is beneficial for cognitive performance in ADHD
Söderlund, G., Sikström, S., & Smart, A. (2007) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01749.x · Cited by 256 · see citing works
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ADHD and Technology Research – Investigated by Neurodivergent Readers
Spiel, K., Hornecker, E., Williams, R. M., & Good, J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517592 · Cited by 150 · see citing works
Children with ADHD and their Care Ecosystem: Designing Beyond Symptoms
Stefanidi, E., Schöning, J., Rogers, Y., & Niess, J. (2023) · International Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581216 · Cited by 61 · see citing works
“…HCI researchers have developed interactive technologies that aim to train executive functions [78, 81], or self-regulation of their behaviour and emotions [22, 23, 43], or establishing effective morning and bedtime routines for increased child independence and lowered parental frustration [70].”
BackgroundDesigning emerging technologies for and with neurodiverse users
Motti, V. (2019) · ACM International Conference on Design of Communication · doi:10.1145/3328020.3353946 · Cited by 53 · see citing works
“McLaren (2017) recommended sound types to help neurodiverse individuals to improve memory and a tt en-SIGDOC tion, minimizing environmental distractions [44].”
BackgroundIntensity and Variable Attention: Counter Narrating ADHD, from ADHD Deficits to ADHD Difference
Bertilsdotter Rosqvist, H., Hultman, L., Österborg Wiklund, S., Nygren, A., Storm, P., & Sandberg, G. (2023) · The British Journal of Social Work · doi:10.1093/bjsw/bcad138 · Cited by 46 · see citing works
Designing for Care Ecosystems: a Literature Review of Technologies for Children with ADHD
Stefanidi, E., Schöning, J., Feger, S., Marshall, P., Rogers, Y., & Niess, J. (2022) · International Conference on Interaction Design and Children · doi:10.1145/3501712.3529746 · Cited by 41 · see citing works
“Technology type Tangible [70, 75, 76], Mobile [17, 46, 58–60, 62], Biofeedback [11, 58, 59], 3D-printing [31], Brain-Computer Interface [3, 47], Wearables [15, 17, 18, 31, 56, 63, 67], Voice-bot [46], Neurofeedback [3, 41], PC software [24, 32, 37], Touch screen [31], Tablet [7], Machine Learning Model [10, 42], Hapti…”
BackgroundMethodInfluentialCharitos, S., Thompson, L., Brigden, A., Russell, A., Slovák, P., & Bird, J. (2025) · International Conference on Interaction Design and Children · doi:10.1145/3713043.3733254 · Cited by 5 · see citing works
Promoting Equitable Learning Outcomes for Underserved Students in Open-Ended Learning Environments
Fonteles, J., Akpanoko, C. E., Wisniewski, P. J., & Biswas, G. (2024) · International Conference on Interaction Design and Children · doi:10.1145/3628516.3655753 · Cited by 2 · see citing works
“McLaren and Antle presented a mixed methods framework for evaluating whether sound can help children with attentional challenges to self-regulate using a neurofeedback system [40].”
BackgroundFidgeting in the Browser: An Empirical Study of a Virtual Self-Regulation Tool for ADHD
Aljedaani, W., Wegener, A., Leonard, W., Ruiz, H., & Eler, M. M. (2026) · International Web for All Conference (W4A) · doi:10.1145/3800424.3800446 · Cited by 1 · see citing works
“Additionally, McLaren and Antle [38] evaluated sound-based neurofeedback (e.g., white noise, binaural beats) as a tool for self-regulation.”
Detection of the level of attention in children with ADHD through brain waves and corporal posture
Garcia, A., González, J. M., & Palomino, A. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219242 · Cited by 1
“Brainwavesarethephysiologicalvariablerelated directlytotheuser’slevelofattentionandbody postureisthephysiologicalvariablethatcanbecor-relatedwiththelevelofattention[22,30,33,34], throughthemathematicalanalysispresentedinthis work.”
Husain, F. (2020) · arXiv.org · doi:10.48550/arxiv.2005.09993 · Cited by 1 · see citing works
“…because of the small sample size, and the questionnaire used in the measurements focused on factualknowledge and missing empathybased measurements McLaren & Antle, 2017 Children with ADHD Evaluating the effect of auditory treatments on school (binaural beats and white noise) on improving…”
BackgroundMétodo experimental para identificar el nivel de atención en las personas
García-Suárez, A., González-Calleros, J.-M., & Palomino, A. (2021) · Rev. Colomb. de Computación · doi:10.29375/25392115.4292
Development of a Multi-user System to Identify the Level of Attention in People
Garcia, A., Gonzalez, J. M., & Palomino, A. (2019) · Research on computing science · doi:10.13053/rcs-148-3-13
“In the implementation of the works [2, 5, 7, 8, 11, 12, 14, 15, 19, 21, 23, 24, 25, 27, 28, 29, 30] re-viewed so far, systems have been developed of mono-user type, which represents a development challenge towards multi-user systems in this context.”
Background
- Authors
- McLaren, E.-S., & Antle, A. N.
- Affiliations
- Simon Fraser University (CA)
- ORCID
- Antle, Alissa N.
- Venue
- Proceedings of the 2017 Conference on Interaction Design and Children
- Publisher
- ACM
- Published
- 2017-06-27
- Pages
- 393-398
- OpenAlex
- W2639813180
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2019: 2
- FWCI
- 0.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Interaction Design and Children (IDC) (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- neurofeedback, children, self-regulation, attention, environmental sounds, binaural beats, white noise, auditory intervention, school environment, memory retrieval
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