Investigating the Need for Modelling Temporal Dependencies in a Brain-Computer Interface with Real-Time Feedback Based on near Infrared Spectra
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Bibliographic record
Abstract
Near infrared (NIR) spectroscopy is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in haemoglobin concentrations in neurocortical tissue. Previous NIR spectroscopy studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. In particular, it is unclear whether or not the temporal dependencies of haemodynamic changes ought to be considered in online classification. To answer this quest ion, this study contrasted online classification of prefrontal haemodynamics using NIR spectra processed using two approaches: an artificial neural network (ANN) that considered instantaneous samples of oxy- and deoxy-haemoglobin concentrations (i.e. ignored temporal dependencies) and a hidden Markov model-based (HMM) classifier which modelled a temporal sequence of concentrations (i.e. embodied temporal dependencies). Both classifiers were implemented for online operation with immediate visual feedback via a monitor showing a vertical bar the height of which was contingent on the classifier's output. Ten subjects participated in two study sessions each, one with each type of classifier. Participants were cued to raise and lower the bar in alternating 20s intervals using mental fast singing and focused breathing, respectively. Only the ANN classifier facilitated online classification rates greater than chance ( P = 0.0289). The influence of physiological noise on online classification of prefrontal haemodynamics was deemed to be minimal via offline analysis of concurrently measured respiration and blood pulse. Nine of the ten participants reported using the feedback to alter their activation strategy. Mental fatigue, task repetitiveness and the lack of ambient lighting were identified as factors compromising performance in half the participants. The inferior performance of the HMM classifiers suggests that modelling of the temporal dynamics of haemoglobin concentration changes may not be necessary in an online NIR-BCI. Further study of online NIR-BCIs with instantaneous feedback is warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it