Fractal Dimension of Resting-State EEG as a Biomarker for Autonomous Sensory Meridian Response (ASMR)
Bibliographic record
Abstract
Autonomous Sensory Meridian Response (ASMR) is an audio-visual phenomenon characterized by multisensory experiences in response to specific auditory stimuli, typically triggering a tingling sensation beginning in the scalp and neck and accompanied by decreased heart rate and deep relaxation. While prior electroencephalogram (EEG) studies have identified ASMR-related neural signatures in stimulus-based paradigms, resting-state differe nces between ASMR-sensitive (ASMR+) and non-sensitive (ASMR-) individuals remain unexplored. In this study, we apply Higuchi's fractal dimension (HFD) to eyes-open and eyes-closed resting-state EEG and demonstrate that ASMR+ participants exhibit significantly lower complexity in the delta (1-4Hz) and theta (4-8Hz) bands and higher complexity in the alpha (8-12Hz) band. Moreover, we train Transformer, Mamba, Random Forest and SVM classifiers on these HFD features to distinguish ASMR+ individuals from ASMR-, achieving F1 scores of 82.56%, 77.33%, 73.93%, and 70.85%, respectively. Finally, using an explainable-AI approach, we showed that ASMR+ participants had significantly lower hubness proportions (network connectivity) than ASMR-. These findings reveal novel resting-state biomarkers of ASMR sensitivity and lay the groundwork for rapid, noninvasive EEG-based screening in ASMR-augmented therapeutic applications. The code has been released on https://github.com/Shyamal-Dharia/Fractal-Dimension-of-Resting-State-EEG-as-a-Biomarker-for-Autonomous-Sensory-Meridian-Response-ASMR-GitHub.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".