Non-invasive brain technologies and their role in clinical applications
Bibliographic record
Abstract
INTRODUCTION: Noninvasive brain monitoring and stimulation techniques are widely used in research and clinical applications. These techniques allow researchers and clinicians to study and affect brain activity safely, without any risk associated with invasive procedures. AREAS COVERED: The present review paper is focused on noninvasive brain monitoring and stimulation techniques. First, noninvasive brain monitoring techniques that are beneficial for clinical experts to detect neurological disorders are highlighted. These techniques also help to identify the exact locations where cognitive functions are disturbed. Second, noninvasive brain stimulation (NIBS) techniques that are provided to the patient's specific locations to improve the cognitive function by modulating the neuronal excitability are also discussed in this paper. EXPERT OPINION: Several clinical studies provide strong evidence related to the improvement of cognitive functions of patients suffering from neurological disorders using NIBS procedures. Various studies are being carried out to upgrade the noninvasive brain monitoring and stimulation techniques combined with artificial intelligence (AI) as transformative tool for advancing personalized portable home-based care, which may be beneficial to improve the lifestyle of patients suffering from the neurological disorders.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".