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The qEEG-Pro System is to be used by qualified clinical professionals for the statistical evaluation of the human electroencephalogram (EEG).

qEEG Pro Database (QPD) is a software program for the post-hoc statistical analysis of the human electroencephalogram (EEG). EEG recorded on a separate device (i.e., the host system) is transferred to the QPD for display and user-review. The device herein described consists of a set of tables that represent the reference means and standard deviations for representative samples. These tables are implemented as computer files that provide access to the exact tabular data resource for use by software that uses the tables as an information resource. The system requires that the user select reliable samples of artifact-free, eyes-closed or eyes open, resting digital EEG for purposes of analysis.

Analysis consists of the Fast-Fourier Transformation (FFT) of the data to extract the spectral power for each of the designated frequency bands (e.g. delta, theta, alpha, and beta), and frequency information from the EEG. The results of this analysis are then displayed in statistical tables and topographical brain maps of absolute and relative power, power asymmetry, and coherence for 19 monopolar and 171 selected bipolar derivations of the EEG. In all over 5,000 measures are derived for comparison against carefully constructed and statistically controlled age-regressed, normative database in which the variables have been transformed and validated for their Gaussian distribution.

Each variable extracted by the analysis is compared to the database using parametric statistical procedures that express the differences between the patient and an appropriate age-matched reference group in the form of z-scores.

Here's an analysis of the acceptance criteria and study detailed in the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: 3 subjects (1 pediatric, 2 adult).
• Data Provenance: The text does not specify the country of origin of the data. It appears to be a prospective study as subjects' EEG recordings were specifically obtained for this validation.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

The text does not explicitly mention any experts being used to establish the ground truth for the test set in the clinical study described. The validation appears to be a direct comparison of the qEEG-Pro's output against the predicate device, K041263, which itself contains a normative database. The "ground truth" in this context is essentially the established output and normative data of the predicate device.

4. Adjudication Method for the Test Set

The text does not describe any adjudication method like 2+1, 3+1, or similar. The validation focuses on comparing the numerical outputs (z-scores for absolute power) of the subject device (qEEG-Pro) against the predicate device (K041263).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, If So, What was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study described is a technical validation comparing the output of the qEEG-Pro software to a predicate device, not a study involving human readers' performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, a standalone study was done. The clinical testing involved comparing the algorithm's output (qEEG-Pro) directly against the predicate device's algorithm output. The "Potential adverse effects" section also highlights the risks if qEEG-Pro is used "as a standalone diagnostic system in the absence of other clinical data from more traditional means of patient evaluation," indicating its standalone capability.

7. The Type of Ground Truth Used

The ground truth used in the clinical testing was the output of a legally marketed predicate device (NeuroGuide Analysis System (NAS), K041263). The comparison was based on z-scores for absolute power calculated for the same EEG data by both the subject and predicate devices.

8. The Sample Size for the Training Set

• qEEG-Pro Normative Database: 1482 samples (eyes-closed); 1231 subjects (eyes-open)
• Predicate Device (NAS) Normative Database: 625 samples

The text specifies these as the sample sizes for the normative databases used by the devices, which serve as their internal "training" or reference data for comparison.

9. How the Ground Truth for the Training Set was Established

The ground truth for the training sets (normative databases) for both the qEEG-Pro and the predicate device was established via "carefully constructed and statistically controlled age-regressed, normative database". These databases contain reference means and standard deviations for representative samples of EEG data. The variables in these databases have been "transformed and validated for their Gaussian distribution." This implies a process of collecting EEG data from a large, healthy population across various age ranges, processing it, and statistically characterizing it to form a reference against which individual patient EEGs are compared to generate z-scores.

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Indicated for prescription use to acquire, record, transmit, and data for electroencephalograph studies of patients of all ages.

The BrainMaster™ Discovery 24 are two-channel, four- channel, and 24-channel electrophysiological acquisition and biofeedback devices. The 24-channel (Discovery 24) digital EEG devices use industry-accepted Windowsbased PC as the control and display console. The devices consist of the following: BrainMaster™ Discovery 24 Hardware Module, BrainMaster™ software - BrainMaster™ BrainAvatar Acquisition software, Electrocap, which is sold separately. The system also includes a full set of DIN-compliant touch-proof sockets, either on the main housing, or in an accessory cable, that allows the use of "free sensors" without a cap. These products are designed for use with a standard IBM PC/AT compatible computers (Windows 7 and later) and associated peripheral equipment such as keyboard, mouse, and monitor(s). When assembled with a computer, the BrainMaster™ Discovery 24 system will acquire EEG and display and save it to disc.

The provided text is a 510(k) summary for the BrainMaster Discovery 24, an electroencephalograph. It details the device's technical specifications and compares it to a predicate device, but it does not contain information about acceptance criteria or a study proving that the device meets those criteria from an AI performance perspective.

The document primarily focuses on:

• Regulatory clearance: Demonstrating substantial equivalence to a predicate device (Cleveland Clinic Vanguard System).
• Device description: Outlining the hardware and software components of the Discovery 24.
• Technical comparison: A table comparing specifications between the Discovery 24 and the predicate device.
• Non-clinical testing: Adherence to IEC standards for electrical medical equipment and software validation.
• Absence of clinical tests: Explicitly stating "Not applicable."

Therefore, I cannot extract the requested information regarding acceptance criteria and a study proving the device meets those criteria as if it were an AI performance study. The device described is a medical instrument for acquiring EEG data, not an AI-driven diagnostic or analytical tool with performance metrics like accuracy, sensitivity, or specificity against established ground truth.

If you are looking for information regarding acceptance criteria related to the technical performance of the EEG device itself (e.g., signal quality, noise levels, sampling rates), these are implicitly covered by the "Non-Clinical Tests Performed" section which references IEC standards and software validation. However, these are not "acceptance criteria" in the context of an AI study measuring diagnostic performance.

In summary, the provided document does not contain the information requested in your bullet points because it is a regulatory submission for a data acquisition device, not a performance study for an AI algorithm.

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The BrainMaster™ 2E is indicated for relaxation training using alpha EEG Biofeedback. In the protocol for relaxation, BrainMaster™ provides a visual and/or auditory signal that corresponds to the patient's increase in alpha activity as an indicator of achieving a state of relaxation.

This two-channel digital Biofeedback/EEG device uses industry-accepted standard Windows - based PC as control and display console. The device consists of the following:

• a) BrainMaster™ 2E Hardware Module
• BrainMaster™ BMT (basic educational/research SW) b)
• BrainMaster™ PT (clinical SW) c)
  The System uses commercially available standard EEG electrodes and pastes such as 9 mm gold disks with 10 - 20 paste.

This 510(k) summary for the BrainMaster™ 2E device indicates that clinical studies were not performed to establish substantial equivalence. Instead, the submission relies on non-clinical tests (electrical, mechanical, and environmental) and comparison to predicate devices, asserting that differences do not affect functionality or safety/effectiveness.

Therefore, many of the requested details about acceptance criteria, study design, and ground truth establishment are not applicable based on the provided document.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present a formal table of acceptance criteria with corresponding device performance metrics in relation to clinical efficacy for relaxation training. Instead, the acceptance criteria are implicitly tied to meeting regulatory standards for electrical, mechanical, and environmental safety, as demonstrated by non-clinical testing, and maintaining substantial equivalence to predicate devices in terms of technical specifications.

2. Sample size used for the test set and the data provenance

Clinical tests were "Not Applicable." Therefore, there was no test set of patient data used to directly evaluate the device's performance in achieving relaxation via alpha EEG biofeedback. The testing focused on device safety and technical specifications, not clinical outcomes.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable, as no clinical test set was used and no clinical ground truth was established for the device's primary intended use. The "ground truth" implicitly referred to here is compliance with technical standards as assessed by a contract testing laboratory.

4. Adjudication method for the test set

Not applicable, as no clinical test set requiring expert adjudication was used.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is an EEG biofeedback system, not an AI-assisted diagnostic imaging tool that would involve human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This refers to the device's technical specifications and electrical/mechanical performance. The device's performance in these non-clinical aspects was tested in a standalone manner by a contract laboratory. The "performance" in this context refers to its ability to meet electrical isolation, EMI/EMC, and basic functional specifications, which it reportedly did.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

For the non-clinical testing, the "ground truth" was compliance with established industry standards (e.g., IEC 60601-1) and FDA guidance documents for medical device safety and performance. This is a technical standard ground truth, not a clinical one based on patient data or expert consensus on clinical outcomes.

8. The sample size for the training set

Not applicable. The device relies on fundamental EEG principles and digital signal processing, not a machine learning model that requires a training set of data.

9. How the ground truth for the training set was established

Not applicable, as there was no training set for a machine learning model.

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