eeg devices used for data collection at the forehead Search Results


forehead eeg device dcm  (Johns Hopkins HealthCare)
90
Johns Hopkins HealthCare forehead eeg device dcm
A . Sensor layout common to many forehead <t>EEG</t> devices such as <t>the</t> <t>Hypnodyne</t> Zmax shown in B. C . PatchEEG by CGX. D . DCM (JHU/APL), an opensource forehead EEG patch designed for realtime processing and sleep research.
Forehead Eeg Device Dcm, supplied by Johns Hopkins HealthCare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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A . Sensor layout common to many forehead EEG devices such as the Hypnodyne Zmax shown in B. C . PatchEEG by CGX. D . DCM (JHU/APL), an opensource forehead EEG patch designed for realtime processing and sleep research.

Journal: bioRxiv

Article Title: ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

doi: 10.1101/2025.06.02.657451

Figure Lengend Snippet: A . Sensor layout common to many forehead EEG devices such as the Hypnodyne Zmax shown in B. C . PatchEEG by CGX. D . DCM (JHU/APL), an opensource forehead EEG patch designed for realtime processing and sleep research.

Article Snippet: To assess generalization to EEG devices beyond the Hypnodyne ZMax, we collected two overnight recordings of co-acquired PSG and a comparable forehead EEG device (DCM, Johns Hopkins University Applied Physics Laboratory, Laurel, MD; shown in ; participant provided informed consent to participate in this study, approved by the Johns Hopkins University’s Institutional Review Board).

Techniques:

A . Accuracy (%) from 5-class model, including dirty data. The same train/test sets were used, but artifact remained unidentified in the training labels, injecting substantial noise into the training set and devastating the model’s capacity to distinguish Wake and REM. B . Accuracy from 5-class model, excluding dirty data. In this example, dirty records were removed from the training set. Superficially this improves performance, but leads to spurious results when applied to noisy data. C . Single example using the classifier in (B). Note how the confusion matrix in (B), which was derived from all 19 participants in the held out test set, looks superb at first glance. The classifier continues to report sleep stages despite there being no meaningful information in signal dropout regions, while also substantially distorting predicted sleep stages in viable signal regions surrounding areas of artifact. This issue may go unnoticed in large datasets, potentially skewing population statistics significantly.) D . EEG spectrogram. E . Ground truth sleep stages from PSG and artifact labeling procedure (see Methods).

Journal: bioRxiv

Article Title: ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

doi: 10.1101/2025.06.02.657451

Figure Lengend Snippet: A . Accuracy (%) from 5-class model, including dirty data. The same train/test sets were used, but artifact remained unidentified in the training labels, injecting substantial noise into the training set and devastating the model’s capacity to distinguish Wake and REM. B . Accuracy from 5-class model, excluding dirty data. In this example, dirty records were removed from the training set. Superficially this improves performance, but leads to spurious results when applied to noisy data. C . Single example using the classifier in (B). Note how the confusion matrix in (B), which was derived from all 19 participants in the held out test set, looks superb at first glance. The classifier continues to report sleep stages despite there being no meaningful information in signal dropout regions, while also substantially distorting predicted sleep stages in viable signal regions surrounding areas of artifact. This issue may go unnoticed in large datasets, potentially skewing population statistics significantly.) D . EEG spectrogram. E . Ground truth sleep stages from PSG and artifact labeling procedure (see Methods).

Article Snippet: To assess generalization to EEG devices beyond the Hypnodyne ZMax, we collected two overnight recordings of co-acquired PSG and a comparable forehead EEG device (DCM, Johns Hopkins University Applied Physics Laboratory, Laurel, MD; shown in ; participant provided informed consent to participate in this study, approved by the Johns Hopkins University’s Institutional Review Board).

Techniques: Derivative Assay, Labeling

The ez6 model demonstrated exemplary performance on a new hardware platform (DCM, JHU/APL) despite having been trained on another platform’s data and not having been fine-tuned or otherwise adjusted to adapt to the new platform. A . Hypnodensity plot of sleep stage probabilities output by ez6 . B . ez6 hypnogram. C . DCM EEG spectrogram, representative of typical DCM recording quality. D . PSG hypnogram used as ground truth for quantifying performance (in this example, accuracy was 76.6% with a Cohen’s κ = .66).

Journal: bioRxiv

Article Title: ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

doi: 10.1101/2025.06.02.657451

Figure Lengend Snippet: The ez6 model demonstrated exemplary performance on a new hardware platform (DCM, JHU/APL) despite having been trained on another platform’s data and not having been fine-tuned or otherwise adjusted to adapt to the new platform. A . Hypnodensity plot of sleep stage probabilities output by ez6 . B . ez6 hypnogram. C . DCM EEG spectrogram, representative of typical DCM recording quality. D . PSG hypnogram used as ground truth for quantifying performance (in this example, accuracy was 76.6% with a Cohen’s κ = .66).

Article Snippet: To assess generalization to EEG devices beyond the Hypnodyne ZMax, we collected two overnight recordings of co-acquired PSG and a comparable forehead EEG device (DCM, Johns Hopkins University Applied Physics Laboratory, Laurel, MD; shown in ; participant provided informed consent to participate in this study, approved by the Johns Hopkins University’s Institutional Review Board).

Techniques: