Limited value of temporo-parietal hemodynamic signals in an optical-electric auditory brain-computer interface
Bibliographic record
Abstract
Objective . Auditory brain-computer interfaces (BCIs) have gained attention recently due to their potential applicability to severely disabled individuals without functional vision. However, auditory BCIs currently achieve lower accuracies than their visual counterparts, and most have exploited only one type of brain measurement. Recent evidence suggests that the combination of electrical and optical measurements can enhance classification accuracies in motor imagery-based BCIs. The potential of this bimodal combination for auditory BCIs remains unexplored. Approach . We investigated the complementarity of near-infrared spectroscopy (NIRS) and electroencephalography (EEG) in discriminating between attentive and non-attentive behaviours to auditory stimuli. Simultaneous NIRS and EEG signals were recorded from 11 typically developed participants while performing an auditory oddball streaming task. Main Results . Considering neural responses to oddballs during the entire span of a trial, average classification accuracies of 77.43+/−9.6% and 80.7+/−9.5% were achieved using combined EEG and NIRS signal and the EEG-only signal respectively. The combined EEG-NIRS classification accuracy was significantly lower than the EEG-only accuracy in five participants. However, when considering EEG responses to the first oddball within each trial, we found that the inclusion of NIRS activities from the complete task period significantly improved classification accuracies for 2 participants. Significance . Our findings suggest that the consolidation of EEG (midline) and NIRS (temporo-parietal) signals offers limited value beyond an EEG-exclusive approach when decoding prolonged auditory attention-demanding tasks. Future efforts should focus on identifying the optimal time window for the analysis of EEG and NIRS signals to delineate conditions under which the BCI may afford a practical advantage over the EEG-exclusive approach.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".