When Thought Becomes Tradeable: Legislating Neuroprivacy Frameworks in the Brain-computer Interfaces (BCIs) Commercial Era
Bibliographic record
Abstract
Neuralink presents brain-computer interfaces (BCIs), the most innovative technology that enables the brain to communicate with the outside world. The global BCI market size is projected to be 8.7 billion in 2033, upwards of the current 2.1 billion in 2024, due to the brain-computer interfaces (BCIs). These systems are associated with medical and cognitive enhancement benefits but are also accompanied by serious concerns about neuro privacy, i.e., unauthorized inference and commercial use of cognitive data. This study seeks to investigate the regulation of commercial BCIs in addressing the value of neuro privacy rights at Neuralink. The study was based on the systematic literature review (SLR) to examine regulatory approaches to neuroprivacy in BCIs. The three databases used in the review are IEEE Xplore, PubMed and Google Scholar search databases between 2014 and 2025. This research examined eight peer-reviewed articles using a strict selection criterion covering relevance, the quality of the methods, and the publication date, allowing detailed information on the issue of neuro privacy in the BCI era to be drawn. The study used thematic analysis to identify and categorise patterns in the literature regarding neuro privacy, control of cognitive information, and commercial BCI ethics. The results showed the different implications and ethical issues of brain-computer interfaces (BCIs) related to the extraction of cognitive data, neuro privacy safeguards, and the commercialization of neural information. The study shows that commercialization of BCIs is faced with some ethical, legal, and privacy issues, and that firm regulation frameworks are necessary to protect neuroprivacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".