Development of a ternary hybrid fNIRS-EEG brain–computer interface based on imagined speech
Bibliographic record
Abstract
There is increasing interest in developing intuitive brain-computer interfaces (BCIs) to differentiate intuitive mental tasks such as imagined speech. Both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used for this purpose. However, the classification accuracy and number of commands in such BCIs have been limited. The use of multi-modal BCIs to address these issues has been proposed for some common BCI tasks, but not for imagined speech. Here, we propose a multi-class hybrid fNIRS-EEG BCI based on imagined speech. Eleven participants performed multiple iterations of three tasks: mentally repeating ‘yes’ or ‘no’ for 15 s or an equivalent duration of unconstrained rest. We achieved an average ternary classification accuracy of 70.45 ± 19.19% which is significantly better than that attained with each modality alone (p < 0.05). Our findings suggest that concurrent measurements of EEG and fNIRS can improve classification accuracy of BCIs based on imagined speech.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".