Towards a hemodynamic BCI using transcranial Doppler without user-specific training data
Bibliographic record
Abstract
UNLABELLED: Transcranial Doppler (TCD) was recently introduced as a new brain-computer interface (BCI) modality for detecting task-induced hemispheric lateralization. To date, single-trial discrimination between a lateralized mental activity and a rest state has been demonstrated with long (45 s) activation time periods. However, the possibility of detecting successive activations in a user-independent framework (i.e. without training data from the user) remains an open question. OBJECTIVE: The objective of this research was to assess TCD-based detection of lateralized mental activity with a user-independent classifier. In so doing, we also investigated the accuracy of detecting successive lateralizations. Approach. TCD data from 18 participants were collected during verbal fluency, mental rotation tasks and baseline counting tasks. Linear discriminant analysis and a set of four time-domain features were used to classify successive left and right brain activations. MAIN RESULTS: In a user-independent framework, accuracies up to 74.6 ± 12.6% were achieved using training data from a single participant, and lateralization task durations of 18 s. SIGNIFICANCE: Subject-independent, algorithmic classification of TCD signals corresponding to successive brain lateralization may be a feasible paradigm for TCD-BCI design.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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 source (direct Gemma or distilled Codex), 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".