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Amydi-med Disposable Non-invasive EEG Sensor is applied directly to an adults/pediatrics patient's skin to record physiological signals (e.g., the electroencephalogram, EEG) in the healthcare facility / hospital.

Amydi-med Disposable Non-invasive EEG Sensor is a low impedance, single patient use, disposable, non-sterile provided sensor that is designed for application to the sites including forehead, temporal, above eyebrow, and mastoid process. It is designed to provide ease of use and accurate electrode placement.

The main components of this sensor are the flexible PCB with electrodes, polyethylene base pad with medical grade pressure sensitive adhesive on its double sides, nylon plastic tine disks, polyurethane foam disks, aqueous gel ( , and cable connector. Each sensor has three (3) to six (6) electrodes on the flexible PCB for different application sites.

The provided text describes the 510(k) summary for the Amydi-med Disposable Non-invasive EEG Sensor (K240668), focusing on its substantial equivalence to a predicate device (K220448). The document highlights performance data for safety and electrical characteristics.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not specify the sample size used for the test sets in the biocompatibility, electrical, adhesive, or shelf-life testing. It also does not provide information on the country of origin of the data or whether the studies were retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This section is not applicable as the device is a medical sensor (hardware) undergoing performance and safety testing, not an AI/software device requiring expert consensus for ground truth establishment.

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

This section is not applicable for the type of device and testing described. Adjudication methods are typically relevant for studies involving human interpretation or assessments where discrepancies need resolution.

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. This type of study is relevant for AI-powered diagnostic or interpretive tools, not for a disposable non-invasive EEG sensor.

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

A standalone performance evaluation (algorithm only) was not done. This refers to AI algorithm performance without human intervention, which is not applicable to a physical medical device like an EEG sensor.

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

The concept of "ground truth" as typically applied to AI/diagnostic studies is not directly applicable here. For hardware testing:

• Biocompatibility: Ground truth is established by the specified standards (e.g., cell viability for cytotoxicity, skin reaction for irritation) and determined by laboratory tests.
• Electrical Performance: Ground truth is defined by the parameters and limits specified in the ANSI/AAMI EC12 standard.
• Adhesive Performance: Ground truth is defined by the parameters and limits specified in the IEC 60601-2-2 standard.
• Shelf Life: Ground truth is determined by the device's ability to meet its performance specifications over time following accelerated aging and real-time stability testing.

8. The sample size for the training set

The document does not mention a training set. This is a physical medical device, not an AI/machine learning model that typically undergoes training.

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

This question is not applicable as there is no mention of a training set for this device.

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