Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.
The 7 matches
- [1] § Machine learning methods › Confidence intervals for accuracy ↔ cmd_analyze_classifier_LOO_pooled.ipynb, lines 334–375 · score 0.76 · confidence intervals, corrected resampled, standard error, freedom, LOO, splits
- [2] § Results › Classification results › TSC-genotype prediction ↔ make_ROC_curves.ipynb, lines 27–112 · score 0.69 · ROC curves, threshold, AUC, C57B6, sensitivity, BOS
- [3] § Machine learning methods › Classification models › Feature transformation via TFIDF ↔ get_classifiers_by_split.py, lines 19–128 · score 0.65 · hyper parameter selection, TFIDF transformation, pipeline, classifier, segments, training
- [4] § Machine learning methods › Bag-of-waves representation › Shift-invariant k-means clustering ↔ si2_kmeans.py, lines 127–196 · score 0.58 · assignment step, best shift, iteration, match, clustering, invariant
- [5] § Machine learning methods › Bag-of-waves representation › Shift-invariant k-means clustering ↔ si2_kmeans.py, lines 30–125 · score 0.55 · Euclidean norm, minimizing, squared, algorithm, shifting, clustering
- [6] § Machine learning methods › Bag-of-waves representation › Shift-invariant k-means clustering ↔ si2_kmeans.py, lines 30–125 · score 0.53 · shift invariant, chosen, minimize, cosine, squared, algorithm
- [7] § Machine learning methods › Classification models › Nested cross-validation for hyper-parameter selection and leave-one-out (LOO) model training ↔ get_classifiers_by_split.py, lines 19–128 · score 0.52 · hyper parameter, selection, internal, TFIDF, LOO, models
Paper
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The authors' code
Python · 326 lines · 12 KB · no license · 3 matches
si2_kmeans.py at commit df37f52, no license · at the source
Overview
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, United States of America
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America
- Department of Neurological Sciences, University of Vermont, Burlington, VT, United States of America
- Neuroscience Program, University of Vermont, Burlington, VT, United States of America
- University of Vermont, Burlington, VT, United States of America
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, United States of America
- The Jackson Laboratory, Bar Harbor, ME, United States of America
- Division of Neuroscience, Nemours Children’s Health, Wilmington, DE, United States of America
Abstract
Objective. Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns—rhythmic and irregular oscillations and transient patterns of sharp waves or spikes—are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (TSC1 gene knockout) in three different inbred strains with distinct genetic backgrounds. Approach. We propose a machine learning approach to predict the genotypes of individual mice from the occurrence counts of waveforms that approximate short windows of the EEG. That is, a dictionary of waveforms is optimized to approximate windows from each genotype, and the vectors of waveform occurrence counts are the features for predicting genotypes via logistic regression models. Main results. Across two-fold cross-validation of the waveform dictionary learning, and leave-one-individual-out
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
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ajbrockmeier/bowaves-mice-genotyping
df37f52509cd7932f8587460ca8a9833d935793f, 6 August 2026Availability: 1 check, the latest on 28 September 2026: the link answers
- 28 September 2026: the link answers
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- cmd_analyze_classifier_L
OO_10_minute.ipynb — Jupyter, 1,002 lines, shown from its source - cmd_analyze_classifier_L
OO_pooled.ipynb — Jupyter, 1,126 lines, 1 match, shown from its source - cmd_analyze_classifier_L
OO_spectral.ipynb — Jupyter, 783 lines, shown from its source - cmd_analyze_shapely_LOO_
pooled.ipynb — Jupyter, 590 lines, shown from its source - get_classifiers_by_split
.py — Python, 362 lines, 2 matches, shown from its source - get_counts_by_split.py — Python, 184 lines, shown from its source
- get_dict_by_split.py — Python, 150 lines, shown from its source
- make_ROC_curves.ipynb — Jupyter, 113 lines, 1 match, shown from its source
- make_metadata_frame.py — Python, 24 lines, shown from its source
- mice_utils.py — Python, 431 lines, shown from its source
- si2_kmeans.py — Python, 326 lines, 3 matches, shown from its source
- si_vq.py — Python, 39 lines, shown from its source
- README.md — Text, 50 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- zenodo:18577633 — at Zenodo; found in “Data availability statement”
Data availability statement
The complete set of code is available at this URL: https://
The data that support the findings of this study are openly available at the following URL/
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 2, 28 September 2026
- Publisher: — → IOP Publishing
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 keywords, 14 MeSH terms, 1 funder, 42 references.
Cite
This paper
Isabel Cano Achuri, M., Kay Lara, M., Abed Rabbo, K., Wilson, B. T., Meek, A., Mahoney, J. M., Hernan, A. E., & Brockmeier, A. J. (2026). Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers. Journal of neural engineering, 23(3), 036016. https://
BibTeX
@article{isabelcanoachur
author = {Isabel Cano Achuri, Maria and Kay Lara, Montana and Abed Rabbo, Khalil and Wilson, Benjamin T and Meek, Austin and Mahoney, J Matthew and Hernan, Amanda E and Brockmeier, Austin J},
title = {{Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers}},
journal = {Journal of neural engineering},
year = {2026},
month = may,
volume = {23},
number = {3},
pages = {036016},
publisher = {IOP Publishing},
issn = {1741-2560},
doi = {10.1088/
url = {https://
pmid = {41780177},
pmcid = {PMC13093256}
}
RIS
TY - JOUR
AU - Isabel Cano Achuri, Maria
AU - Kay Lara, Montana
AU - Abed Rabbo, Khalil
AU - Wilson, Benjamin T
AU - Meek, Austin
AU - Mahoney, J Matthew
AU - Hernan, Amanda E
AU - Brockmeier, Austin J
TI - Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers
T2 - Journal of neural engineering
J2 - J Neural Eng
PY - 2026
DA - 2026/
VL - 23
IS - 3
SP - 036016
SN - 1741-2560
PB - IOP Publishing
DO - 10.1088/
UR - https://
LA - en
ER -
CSL-JSON
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