Detection of Consciously Controlled Motor Cortical Activation by Near Infrared Spectroscopy (NIRS)
Bibliographic record
Abstract
Custom headgear was developed and fabricated for the purposes of detecting the hemodynamic response in the motor cortex of the human brain by near infrared spectroscopy (NIRS). We present here, results from this NIRS study. Light in the NIR region was incident upon the human motor cortex in anticipation of observing differences in the absorption curves for recordings of periodic motor activation with respect to periods of rest. Frequency domain NIRS was used to obtain the properties of the NIR signal after the light waves had traversed the brain tissue. Analysis of the intensity of the signal reveals that the absorptive properties of the tissue are altered during periods of activation. T en neurologically healthy individuals each performed both a resting and a motor cortical activation task. We examined the shape of the average time domain curve for each of the two tasks and observe distinct differences; therefore, we anticipate that feature differences between these two curves will consistently discriminate between activation task and baseline responses. In particular, we examined the distribution of the peak-to-peak ranges, inter-peak times, the slopes of the average curves, and the magnitude of the extrema with respect to the mean value. These feature differences may be harnessed to discern a binary signal, thereby demonstrating the hemodynamic signal’s potential as an access pathway enabling individuals with severe motor disability.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".