Brain-Computer Interfaces, Inclusive Innovation, and the Promise of Restoration: A Mixed-Methods Study with Rehabilitation Professionals
Bibliographic record
Abstract
Over the last two decades, researchers have promised “neuroprosthetics” for use in physical rehabilitation and to treat patients with paralysis. Fulfilling this promise is not merely a technical challenge but is accompanied by consequential practical, ethical, and social implications that warrant sociological investigation and careful deliberation. In response, this paper explores how rehabilitation professionals evaluate the development and application of BCIs. It thereby also asks how the BCIs come to be seen as desirable or not, and implicitly, what types of persons, rights, and responsibilities are assumed in this discourse. To this end, we conducted a web-based survey (N=135) and follow-up interviews (N=15) with Canadian professionals in physical therapy, occupational therapy, and speech-language pathology. We find that rehabilitation professionals, like other publics, express hope and enthusiasm regarding the use of BCIs for assistive purposes. They envision BCI devices as powerful means to reintegrate patients and disabled people into social life but also express practical and ethical reservations about the technology, positioning themselves as uniquely qualified to inform responsible BCI design and implementation. These results further illustrate the nascent “co-production” of neural technologies and social order. More immediately, they also pose a serious challenge for implementing frameworks of responsible innovation; merely prescribing more inclusive technology development may not counteract technocratic processes and widely held ableist views about the need to augment certain bodies using technology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".