Limited value of temporo-parietal hemodynamic signals in an optical-electric auditory brain-computer interface
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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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it