Multichannel wearable f<scp>NIRS‐EEG</scp>system for long‐term clinical monitoring
Bibliographic record
Abstract
Continuous brain imaging techniques can be beneficial for the monitoring of neurological pathologies (such as epilepsy or stroke) and neuroimaging protocols involving movement. Among existing ones, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have the advantage of being noninvasive, nonobstructive, inexpensive, yield portable solutions, and offer complementary monitoring of electrical and local hemodynamic activities. This article presents a novel system with 128 fNIRS channels and 32 EEG channels with the potential to cover a larger fraction of the adult superficial cortex than earlier works, is integrated with 32 EEG channels, is light and battery-powered to improve portability, and can transmit data wirelessly to an interface for real-time display of electrical and hemodynamic activities. A novel fNIRS-EEG stretchable cap, two analog channels for auxiliary data (e.g., electrocardiogram), eight digital triggers for event-related protocols and an internal accelerometer for movement artifacts removal contribute to improve data acquisition quality. The system can run continuously for 24 h. Following instrumentation validation and reliability on a solid phantom, performance was evaluated on (1) 12 healthy participants during either a visual (checkerboard) task at rest or while pedalling on a stationary bicycle or a cognitive (language) task and (2) 4 patients admitted either to the epilepsy (n = 3) or stroke (n = 1) units. Data analysis confirmed expected hemodynamic variations during validation recordings and useful clinical information during in-hospital testing. To the best of our knowledge, this is the first demonstration of a wearable wireless multichannel fNIRS-EEG monitoring system in patients with neurological conditions. Hum Brain Mapp 39:7-23, 2018. © 2017 Wiley Periodicals, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".