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The HBldb product is to be used by qualified medical professionals for the post-hoc statistical evaluation of the human electroencephalogram (EEG), utilizing evoked response potentials (ERP).

The Human Brain Index Software (HBldb) product is a software program for the post-hoc statistical analysis and comparison with a reference data (the Human Brain Index Reference Database (HBIRD)) of the human electroencephalogram (EEG) including spontaneous oscillations of the human brain potentials and event-related potentials (ERPs). The EEG is recorded on a separate device under the standardized HBIdb conditions and is transferred to the HBIdb in EDF+ format for

• display .
• . spectral analysis and
• analysis of event-related potentials .
• comparison of the gathered parameters against the Human Brain Index Reference Database . (HBIRD), and
• compilation of a report. .
  Results of comparison of individual EEG/ERP parameters with the database are intended for use as an aid to diagnosis. No medication or treatment is applied based on this comparison alone. The results have to be considered only in conjunction with other clinical findings.

Here's an analysis of the provided text regarding the acceptance criteria and study for the HBIdb software:

Important Note: The provided document is a 510(k) summary for a medical device. This type of submission focuses on demonstrating substantial equivalence to existing, legally marketed predicate devices, rather than proving efficacy or meeting specific performance acceptance criteria through clinical trials. Therefore, the information typically found in a clinical study report regarding detailed acceptance criteria, effect sizes, and deep dives into ground truth establishment is largely absent. The "acceptance criteria" here implicitly refer to demonstrating equivalence in intended use, technology, and performance with predicate devices.

Acceptance Criteria and Reported Device Performance

Since this is a 510(k) summary, formal "acceptance criteria" in the sense of specific numerical thresholds for performance metrics are not explicitly stated. The comparison is made against predicate devices, focusing on equivalency of features and intended use. The "device performance" is therefore implicitly that it functions similarly to the predicate devices.

Study Information:

1. Sample sizes used for the test set and the data provenance:

   • Test Set Sample Size: The document does not specify a numerical sample size for a "test set" in terms of subject data. It states, "For verification and validation of the product, all software functions were tested by means of specific EEG test files." This implies a set of pre-defined, synthetic, or existing EEG data files were used to test the software's functionality, not necessarily a clinical dataset for performance evaluation.
   • Data Provenance: Not specified for these "specific EEG test files." Given the manufacturer is German, the test environment and data could be internal or from Germany, but this is not directly stated. The data used for the Human Brain Index Reference Database (HBIRD) is not described here, only that the device compares against it.

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

   • Not applicable/Not specified. The testing described focuses on software functionality (verification and validation against IEC and FDA guidance), not on diagnostic accuracy of the device's output against a clinical "ground truth" established by experts. The HBIdb product aids diagnosis but doesn't provide a definitive diagnosis on its own.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable/Not specified. Since the testing was for software functionality rather than clinical diagnostic performance adjudicated by experts, no such method is mentioned.

4. 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:

   • No. An MRMC comparative effectiveness study was not performed or described in this 510(k) summary. This type of study demonstrates clinical utility with human readers, which is beyond the scope of this type of premarket submission which is focused on substantial equivalence.

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

   • Yes, in a sense. The testing performed was "standalone" in that it validated the software's functions (e.g., display, spectral analysis, ERP analysis, comparison against the HBIRD, report compilation) using "specific EEG test files." This focused on the algorithm's correct execution and output formation, not on diagnostic accuracy by itself. The device is an "aid to diagnosis" and meant to be used by "qualified medical professionals," indicating that human interpretation is always in the loop for diagnosis.

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

   • For the software's functional testing, the "ground truth" would be the expected output or behavior of the software for the "specific EEG test files" based on its design specifications and algorithms. This is a technical ground truth, not a clinical diagnostic ground truth. The HBIdb compares data against a Human Brain Index Reference Database (HBIRD), which is a form of ground truth (normative data), but the method of establishing that database's ground truth is not detailed in this document.

7. The sample size for the training set:

   • Not specified. The document does not mention a "training set" for the HBIdb software itself. This suggests the software is likely rule-based or statistical in nature, comparing inputs against a pre-established "reference database" (HBIRD), rather than a machine learning model that requires a distinct training phase with labeled data. The HBIRD itself would have been built from a large sample, but its size is not in this document.

8. How the ground truth for the training set was established:

   • Not applicable, as a distinct "training set" for a machine learning model is not described. The HBIdb compares data against the Human Brain Index Reference Database (HBIRD). How the "ground truth" for this normative database (HBIRD) was established (e.g., participant selection, EEG acquisition protocols, data processing, statistical methods for defining "normative ranges") is beyond the scope of this 510(k) summary and not provided.

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