Dynamic topographical pattern classification of multichannel prefrontal NIRS signals
Bibliographic record
Abstract
OBJECTIVE: Near-infrared spectroscopy (NIRS) is an optical imaging technique that has recently been considered for brain-computer interface (BCI) applications. To date, NIRS-BCI studies have primarily made use of temporal features of brain activity, derived from the time-course of optical signals measured from discrete locations, to differentiate mental states. However, functional brain imaging studies have indicated that the spatial distribution of haemodynamic activity is also rich in information. Thus, the progression of a response over both time and space may be valuable to brain state classification. In this paper, we investigate the implication of including spatiotemporal features in the single-trial classification of haemodynamic events for a two-class problem by exploiting this information from dynamic NIR topograms. APPROACH: The value of spatiotemporal information was explored through a comparative analysis of four different classification schemes performed on multichannel NIRS data collected from the prefrontal cortex during a mental arithmetic activation task and rest. Employing a linear discriminant classifier, data were analysed using spatiotemporal features, temporal features, and a collective pool of spatiotemporal and temporal features. We also considered a majority vote combination of three classifiers; each established using one of the above feature sets. Lastly, two separate task durations (20 and 10 s) were considered for feature extraction. MAIN RESULTS: With features from the longer task interval, the highest overall classification accuracy was achieved using the majority voting classifier (76.1 ± 8.4%), which was greater than the accuracy obtained using temporal features alone (73.5 ± 8.5%) (F3,144 = 7.04, p = 0.0002). While results from the shorter task duration were lower overall, the classifier employing only spatiotemporal features (with an average accuracy of 67.9 ± 9.3%) achieved a higher average accuracy than the rate obtained using only temporal features (64.4 ± 8.4%) (F3,144 = 18.58, p < 10(-4)). SIGNIFICANCE: Collectively, these results suggest that spatiotemporal information can be of value in the analysis of functional NIRS data, and improved classification rates may be obtained in future NIRS-BCI applications with the inclusion of this information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".