Comparison of Neural Tracking and Spectral Entropy in Patients with Disorders of Consciousness
Bibliographic record
Abstract
Objectives: This study investigates brain responses to natural speech in patients with disorders of consciousness (DoC), focusing on the speech envelope. Two measures were used: neural tracking, which evaluates how well brain activity follows the speech envelope, and spectral entropy, which assesses the complexity of brain responses. These measures were compared to clinical diagnosis and behavioral responsiveness, assessed via the Coma Recovery Scale-Revised (CRS-R). Design: Four DoC patients underwent electroencephalography (EEG) recording while listening to a narrated story in Dutch and Swedish, alongside baseline EEG. We employed both spectral entropy and a backward modeling approach to evaluate the speech envelope’s neural tracking. This technique involves training a model to map the relationship between EEG signals and the corresponding speech envelope. Once the model is trained, it can use unseen EEG data to reconstruct the speech envelope, which is then compared to the original speech envelope to assess how effectively the patient processed the auditory stimulus. For the behavioral assessment, CRS-R scores were converted into the CRS-R index. Results: Spectral entropy positively correlated with the CRS-R index during listening. Neural tracking correlated with CRS-R diagnoses but not the index. An interaction showed that higher neural tracking strengthened the link between spectral entropy and behavioral responsiveness. Conclusion: This study demonstrated the potential of neural tracking and spectral entropy as complementary tools to investigate patients with DoC. Spectral entropy proved valuable for assessing behavioral responsiveness, while neural tracking shows promise in assessing the DoC diagnosis.
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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 | 0.003 |
| 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".