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The Crystal-EEG Model 10 is a mobile, intermediate range, wireless EEG system intended to be used for measuring and transmitting electroencephalogram (EEG) signals.

Device Description

The Crystal-EEG Model 10 is a mobile, intermediate range, wireless, EEG system used for measuring and transmitting bioelectric signals such as electroencephalogram (EEG). It consists of a Transmitter, a Receiver Assembly which consists of the receiver, receiver cable, and power supply; the Software; Patient Accessories consisting of EEG Electrodes, headband, battery, and battery connector; and a PC Operator Interface which consists of Operator Interface Software, Transmitter Set-up Cable, and a Personal Computer (optional).

AI/ML Overview

The provided document describes the Crystal-EEG Model 10, an electroencephalograph (EEG) system. The acceptance criteria and the study proving the device meets these criteria are outlined in the "Bench Testing" section.

Acceptance Criteria and Reported Device Performance

Acceptance Criteria	Reported Device Performance
Noise Amplitude: Low noise in transmitted signals.	The RMS of the noise for all 24 channels (8 channels per transmitter for three transmitters with shorted input channels) was in the order of 1 µVolt. (Figure 1 shows the visual representation of this low noise, fluctuating around 0 µV).
Signal Transmission Accuracy: Ability to accurately measure and transmit known signals (sine waves).	The device successfully measured and transmitted known analog sine waves at 5 Hz and 20 Hz, showing that "the sine waves were measured and transmitted across the link." (Figures 2 and 3 visually represent these transmitted sine waves).
Filter Functional Accuracy: Accurate function of low and high pass filters, demonstrating expected attenuation at spectral ends.	The system's frequency response was evaluated by varying a sine wave frequency from 0.12 Hz to 80 Hz and recording peak amplitudes. The data in Table 1 shows expected levels of attenuation at both low (e.g., 12 µV at 0.12 Hz for a 100 µV input) and high frequencies (e.g., 3 µV at 80 Hz for a 100 µV input), verifying the accuracy of the implemented filters. The peak amplitudes at central frequencies (e.g., 98-100 µV between 10-20 Hz) indicate accurate unattenuated transmission within the intended band.

Study Information

Sample size used for the test set and the data provenance:
- Noise Testing: Three Crystal-EEG Model 10 Transmitters were used, totaling 24 channels (8 channels/Transmitter). The input channels were shorted.
- Signal Transmission Testing: Details on the number of individual tests or channels used for sine wave transmission are not explicitly stated beyond "one typical channel" for the 5 Hz and 20 Hz examples.
- Filter Accuracy Testing: One Crystal-EEG Model 10 unit was evaluated.
- Data Provenance: The testing was "Bench Testing," implying a controlled laboratory environment. The country of origin of the data is not specified but is presumed to be internal testing by Cleveland Medical Devices Inc. in Cleveland, Ohio, USA, where the company is located. The nature of the testing is prospective, as it involved actively generating and measuring signals.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- No human experts were used to establish the ground truth for the bench testing. The ground truth was established by known electrical signals (shorted inputs for noise, signal generator for sine waves with known frequencies and amplitudes). This is an objective, instrumental ground truth, not reliant on expert interpretation.
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not applicable. As the ground truth was established by objective electrical measurements and signal generation, no human adjudication was required.
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. This device is an electroencephalograph (EEG) system, a medical device for measuring bioelectric signals. The provided document details its technical performance characteristics through bench testing. It is not an AI-assisted diagnostic tool, and therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable to this submission.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, this was effectively a standalone performance evaluation of the device's hardware and software components (apart from the PC operator interface being optional, the core function is the device itself). The "Bench Testing" specifically assesses the device's ability to measure, transmit, and process electrical signals without human interpretation as part of the primary test.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Objective Instrumental Ground Truth: The ground truth was established by known electrical parameters.
  - For noise: A shorted input represents a theoretically "zero signal" condition, allowing measurement of intrinsic system noise.
  - For signal transmission: A signal generator provides precisely controlled sine waves of known frequency and amplitude.
  - For filter accuracy: A signal generator provides sine waves of known frequencies and a theoretical model predicts the expected attenuation based on the filter design.
The sample size for the training set:
- Not applicable. The device described is an EEG signal acquisition and transmission system, not a machine learning or AI-based system that requires a "training set" in the conventional sense. The "Bench Testing" focuses on the fundamental electrical performance characteristics of the hardware and integrated software.
How the ground truth for the training set was established:
- Not applicable, as there was no training set.

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