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The EEG-1200A Series Neurofax is intended to record, measure and display cerebral and extracerebral activity for EEG and Sleep Studies. These data may be used by the clinician in Sleep Disorder, Epilepsies and other related disorders as an aid in diagnosis. The device is intended for use by medical personnel in any location within a medical facility, laboratory, clinic or nursing home or outside of a medical facility under direct supervision of a medical professional.

The EEG-1200A Electroencephalograph is the instrument to provide the information for intervention to utilize derivation, recording, analysis, or each combination of the brain's action potential. The instrument can measure the vital signal (including ECG waveform, EMG waveform, respiration waveform, ocular motility, SpO2 and CO2) in relation to EEG examination and display the waveform on the screen changing the montage and amplifier conditions. In addition, measurement data is available to file into the electric media. Changing the montage and amplifier conditions, then record to the printer, previews the measured data filed into the electric media. EEG data analysis is also executed to use the analysis software.

The provided text describes the Nihon Kohden EEG-1200A Series Neurofax, an electroencephalograph (EEG) device. It includes a 510(k) summary, intended use, and a comparison to a predicate device. However, it does not contain information about specific acceptance criteria, a detailed study proving device performance against such criteria, sample sizes for test or training sets, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies for AI devices.

The "study" mentioned in the document refers to engineering verification and validation testing, not clinical performance studies with human subjects or AI-specific evaluations.

Therefore, I will extract relevant information from the provided text to construct the table and address the questions as much as possible, indicating when information is "Not provided" by the source.

Acceptance Criteria and Reported Device Performance

Note: The "acceptance criteria" here are derived from the comparison table highlighting the technological characteristics of the new device relative to its predicate. The "study" proving these criteria were met is described as electromagnetic, environmental, safety, and performance testing, along with software validation.

Study Details

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

   • Not provided. The 510(k) summary mentions "electromagnetic, environmental, safety and performance testing procedures" and "Software validation tested the operation of the software functions," but it does not specify sample sizes for any test sets in a clinical or data-driven context. Given that this is a hardware device review, not an AI device, traditional "test sets" of patient data are not discussed in this submission.

2. 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):

   • Not provided. As this is a hardware device submission, expert-established ground truth for clinical data is not a component of the described testing.

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

   • Not applicable / Not provided. Adjudication methods are typically relevant for human-read or AI-assisted interpretation of clinical data, which is not the focus of this 510(k) summary.

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 study was not done, as this device is an electroencephalograph, not an AI-assisted diagnostic tool.

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

   • Not applicable. This device is hardware for acquiring and displaying EEG data, not an algorithm for standalone interpretation. The software validation mentioned focuses on the correct operation of device functions (acquiring, processing, displaying, recording), not on diagnostic interpretation.

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

   • Not applicable / Not provided. The "ground truth" in the context of this submission refers to engineering specifications and verification that the device operates within those specifications (e.g., noise levels, frequency response), as opposed to clinical diagnoses.

7. The sample size for the training set:

   • Not provided / Not applicable. This device is not an AI/ML product developed using training data in the conventional sense.

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

   • Not applicable. This device does not have a "training set" in the context of machine learning. The "ground truth" for its operation is based on established engineering and safety standards, against which its performance was tested.

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