OSCR

At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Methods › Sleep Macrostructure and Microstructure Analysis › Sleep Microarchitecture Analysis ↔ SleepPatch_EEG_OSF_v1.0/04_analysis_code.zip/04_analysis_code/relative_bandpower_osf.ipynb, lines 27–81 · score 0.87 · relative delta power, 0.5–45 Hz, bandpass filtered, Beta, Theta, bands
  2. [2] § Methods › Sleep Macrostructure and Microstructure Analysis › Sleep Microarchitecture Analysis ↔ SleepPatch_EEG_OSF_v1.0/04_analysis_code.zip/04_analysis_code/slow_wave_absolutethreshold_osf.ipynb, lines 27–146 · score 0.82 · 0.5–1.5 Hz, 0.5–2 seconds, N3 sleep stages, zero, amplitude, root
  3. [3] § Methods › Sleep Macrostructure and Microstructure Analysis › Automated Sleep Stage Scoring ↔ SleepPatch_EEG_OSF_v1.0/04_analysis_code.zip/04_analysis_code/artifact_rejection_osf.ipynb, lines 27–112 · score 0.66 · artifact cleaned EEG, artifact detection, threshold, windows, channel, scored
  4. [4] § Methods › Initial Assessment of Data Quality ↔ SleepPatch_EEG_OSF_v1.0/04_analysis_code.zip/04_analysis_code/multitaper_spectrogram.m, lines 1–141 · score 0.56 · multitaper spectrograms, Prerau, nose, power, 0.3 Hz, sleep
  5. [5] § Methods › Sleep Macrostructure and Microstructure Analysis › Automated Sleep Stage Scoring ↔ SleepPatch_EEG_OSF_v1.0/04_analysis_code.zip/04_analysis_code/slow_waves_zscore75thpercentile_osf.ipynb, lines 27–164 · score 0.53 · bandpass filtered, Sleep stage, threshold, detection, REM, epochs

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 82 lines · 3.1 KB · no license · 1 match

  1. # %%
  2. from pathlib import Path
  4. # === USER SETTINGS (edit these) ===
  5. PROJECT_ROOT = Path('.') # set to the OSF dataset root (this notebook assumes it is run from that root)
  6. EDF_DIR = PROJECT_ROOT / '02_clean_edf'
  7. OUT_DIR = PROJECT_ROOT / 'outputs'
  8. OUT_DIR.mkdir(parents=True, exist_ok=True)
  10. subject_id = 'sub_###'
  11. session_id = 'ses-##'
  13. # Inputs
  14. edf_path = EDF_DIR / f"{subject_id}_{session_id}_clean.edf"
  16. # Outputs / intermediates
  17. stage_csv_path = OUT_DIR / f"{subject_id}_{session_id}_sleepstage_yasa.csv"
  18. combined_csv_path = PROJECT_ROOT / 'PATH_TO_COMBINED_EEG_CSV.csv' # used in CSV-based preprocessing notebooks
  19. artifact_clean_csv_path = OUT_DIR / f"{subject_id}_{session_id}_artifact_clean.csv"
  22. # %%
  23. # NOTE: If needed, install dependencies:
  24. # !pip install --upgrade yasa mne numpy pandas matplotlib
  27. # %%
  28. import mne
  29. import yasa
  30. import numpy as np
  31. import pandas as pd
  33. # Load EEG data from the EDF file
  34. raw = mne.io.read_raw_edf(str(PROJECT_ROOT / 'PATH_HERE'), preload=True, verbose=0)
  35. raw.pick('eeg') # Correct way to select EEG channels using the new API
  36. raw.filter(0.5, 45) # Bandpass filter from 0.5 to 45 Hz
  37. sf = raw.info['sfreq'] # Sampling frequency
  39. # Load staged data (hypnogram) from CSV file
  40. hypno_df = pd.read_csv(stage_csv_path) # Load with headers
  42. # Ensure the hypnogram has a 'Sleep Stage' column, otherwise raise an error
  43. if 'Sleep Stage' not in hypno_df.columns:
  44. raise KeyError("The 'Sleep Stage' column is missing from the hypnogram CSV file.")
  46. hypno = hypno_df['Sleep Stage'].values # Extract the 'Sleep Stage' column
  48. # Map the hypnogram string labels to integer values
  49. stage_mapping = {'W': 0, 'N1': 1, 'N2': 2, 'N3': 3, 'R': 4}
  50. hypno = np.array([stage_mapping[stage] for stage in hypno]) # Map string stages to integers
  52. # Upsample the hypnogram to match the data's sampling frequency if needed
  53. hypno_up = yasa.hypno_upsample_to_data(hypno=hypno, sf_hypno=(1/30), data=raw.get_data(), sf_data=sf)
  55. # Define masks for the N2 and N3 stages
  56. mask_N2 = hypno_up == 2
  57. mask_N3 = hypno_up == 3
  59. # Apply the mask to the data and restrict the data to N2 and N3 segments
  60. raw_N2 = raw.copy().crop(tmin=np.where(mask_N2)[0][0] / sf, tmax=np.where(mask_N2)[0][-1] / sf)
  61. raw_N3 = raw.copy().crop(tmin=np.where(mask_N3)[0][0] / sf, tmax=np.where(mask_N3)[0][-1] / sf)
  63. # Calculate relative bandpower for multiple bands, focusing on Delta, Theta, Alpha, Beta bands
  64. bands = [(0.5, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'), (12, 30, 'Beta')]
  66. bp_N2 = yasa.bandpower(raw_N2, sf=sf, ch_names=raw.ch_names, bands=bands, relative=True)
  67. bp_N3 = yasa.bandpower(raw_N3, sf=sf, ch_names=raw.ch_names, bands=bands, relative=True)
  69. # Display the relative bandpower for N2 and N3 stages separately for each channel
  70. print("Relative Bandpower for N2 Stage (by channel):")
  71. print(bp_N2)
  73. print("\nRelative Bandpower for N3 Stage (by channel):")
  74. print(bp_N3)
  76. # Calculate average relative delta power across channels for N2 and N3 stages
  77. avg_delta_N2 = bp_N2['Delta'].mean()
  78. avg_delta_N3 = bp_N3['Delta'].mean()
  80. print("\nAverage Relative Delta Bandpower for N2 Stage (across channels):", avg_delta_N2)
  81. print("Average Relative Delta Bandpower for N3 Stage (across channels):", avg_delta_N3)

relative_bandpower_osf.ipynb, no license · at the source

Overview

Authors: Hyeonseok Kim1, Simran Saha2, Aiden Wachnin2, Seunghyeb Ban1, Yoon Jae Lee1, Youngjin Kwon1, Jaeho Lee1, Isha Chhabra2, Sahana Ram2, Chuu Nyan2, Lynn Marie Trotti3, Woon-Hong Yeo1, Audrey Duarte2
ORCID iDs: Hyeonseok Kim, Simran Saha, Aiden Wachnin, Seunghyeb Ban, Yoon Jae Lee, Youngjin Kwon, Jaeho Lee, Isha Chhabra, Sahana Ram, Chuu Nyan, Lynn Marie Trotti, Woon-Hong Yeo, Audrey Duarte
  1. George W. Woodruff School of Mechanical Engineering, Wearable Intelligent Systems and Healthcare Center (WISH Center), Georgia Institute of Technology, Atlanta, GA, United States
  2. Department of Psychology, The University of Texas at Austin, 108 East Dean Keeton Street NW, Austin, TX, 78712, United States, 1 5122324643
  3. Department of Neurology, Emory Sleep Center, Emory University, Atlanta, GA, United States
Institutions: Georgia Institute of Technology (United States); The University of Texas at Austin (United States); Emory University (United States)
Journal: JMIR formative research, volume 10, article e80286
Dates: received 9 July 2025; accepted 17 March 2026; published online 4 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.2196/80286 · PMID 42081802 · PMCID PMC13150960 · OpenAlex W7138079816
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Physiology & signal measures
Keywords: Aging, Electroencephalography, Sleep, Validation, Naturalistic, Sleep Patterns, Wearables
MeSH: Electroencephalography*, Polysomnography*, Wireless Technology*, Adult, Aged, Female, Humans, Male, Middle Aged, Pilot Projects, Young Adult (* major topic)
Topic: Obstructive Sleep Apnea Research (Physiology, Medicine), according to OpenAlex
Funding: NIA NIH HHS (R21 AG064309)
Citations: cited by 1 paper (Europe PMC); 42 references in the paper

Abstract

Background: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night.

Objective: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature.

Methods: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant.

Results: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U=42.0; P=.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U=45.0; P=.006).

Conclusions: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants’ homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF gjcyu

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 27 files
Software Heritage: not checked
Found in: the references
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), pandas (7 files), YASA (7 files), Signal Processing Toolbox (6 files), MNE-Python (6 files), Matplotlib (5 files), seaborn (4 files), EEGLAB (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
20 files
At the source: osf.io/gjcyu/overview

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 13 authors, 7 keywords, 11 MeSH terms, 1 funder, 39 references.

Cite

This paper

Kim, H., Saha, S., Wachnin, A., Ban, S., Lee, Y. J., Kwon, Y., Lee, J., Chhabra, I., Ram, S., Nyan, C., Trotti, L. M., Yeo, W.-H., & Duarte, A. (2026). At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study. JMIR formative research, 10, e80286. https://doi.org/10.2196/80286

BibTeX

@article{kim2026home,
author = {Kim, Hyeonseok and Saha, Simran and Wachnin, Aiden and Ban, Seunghyeb and Lee, Yoon Jae and Kwon, Youngjin and Lee, Jaeho and Chhabra, Isha and Ram, Sahana and Nyan, Chuu and Trotti, Lynn Marie and Yeo, Woon-Hong and Duarte, Audrey},
title = {{At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study}},
journal = {JMIR formative research},
year = {2026},
month = may,
volume = {10},
pages = {e80286},
publisher = {JMIR Publications Inc.},
issn = {2561-326X},
doi = {10.2196/80286},
url = {https://doi.org/10.2196/80286},
pmid = {42081802},
pmcid = {PMC13150960}
}

RIS

TY - JOUR
AU - Kim, Hyeonseok
AU - Saha, Simran
AU - Wachnin, Aiden
AU - Ban, Seunghyeb
AU - Lee, Yoon Jae
AU - Kwon, Youngjin
AU - Lee, Jaeho
AU - Chhabra, Isha
AU - Ram, Sahana
AU - Nyan, Chuu
AU - Trotti, Lynn Marie
AU - Yeo, Woon-Hong
AU - Duarte, Audrey
TI - At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study
T2 - JMIR formative research
J2 - JMIR Form Res
PY - 2026
DA - 2026/05/04
VL - 10
SP - e80286
SN - 2561-326X
PB - JMIR Publications Inc.
DO - 10.2196/80286
UR - https://doi.org/10.2196/80286
LA - en
ER -

CSL-JSON

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