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Record W7053325768

Towards Improving Access to Assistive Communication through Brain Computer Interface Technology, Interface Design, and Policy

2021· dissertation· W7053325768 on OpenAlexfundno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersBloorview Research Institute
KeywordsInterface (matter)Augmentative and alternative communicationBrain–computer interfaceUser interfacePopulationInterface designIdentification (biology)Sample (material)Natural language user interface
DOInot available

Abstract

fetched live from OpenAlex

Communication is the foundation for many daily activities. Individuals with complex communication needs often require alternative communication pathways to support self-expression, education, and enjoyment of life. Children are especially vulnerable to the effects of impeded communication. Augmentative and alternative communication (AAC) devices can improve a child's development. This thesis explores strategies for improving the efficiency of and access to brain-mediated communication technology for pediatric users. Four self-contained papers are included in addition to a brief chapter discussing natural language processing models designed for pediatric communication. The first paper applied ecological interface design (EID) to investigate system boundaries and user capacity required for audible language-based communication. This research exposed unmet needs in language organization for efficient communication support and yielded a new AAC interface design. Study two evaluated the change in usability, mental workload, and information transfer rate enabled by the introduced EID interface in comparison to a commercial AAC interface. A significant improvement in communication experience during EID interface use was observed in 5 out of 7 performance measures. Study three investigated a new word selection pathway through electroencephalographic (EEG) identification of semantic priming. Above-chance accuracies in excess of 70 % were achieved in automatic word preference discrimination on the basis of 2-trial averages in a sample of pediatric participants. Findings from this study suggest that semantic priming can potentially be leveraged in a BCI system to provide automatic feedback in line with users’ language preferences. The final paper considered the distribution of and population access to brain-mediated AAC technology, by analyzing technology promotion and adoption pathways. I presented policy recommendations to ensure equity in distribution. Together, the 4 papers in this thesis contribute new knowledge about mitigating the access barriers to brain-based communication.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0070.007
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.409
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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