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The NeuroLink NeuroMonitoring System has the same intended use as the predicate device, the HydroDot NeuroMonitoring System. Both are designed to locate EEG electrodes according the 10-20 International System and present electrical signal sensed by the electrodes from the skin to existing EEG recording, analysis and archiving equipment.

The e-Net NeuroMonitoring System consists of a set of components that work together to acquire and present EEG signal to any existing EEG recording and analysis equipment. The components in the original submission included Biosensor electrodes, e-Net headpiece, Patient Module, fiber optic interface, Monitor Module, AC power supply, and EEG Adapter Cable. The components of this submission include Biosensor electrodes, e-Net headpiece, Patient Module, fiber optic interface, and DSP Interface Card (replaces Monitor Module). The AC power supply and EEG Adapter Cable have been eliminated.
The modified system discussed in this submission uses the same electrodes and headpiece for signal acquisition but has changes in the modules used to transmit signals to the existing EEG recording and analysis equipment.
The Patient Module is a small battery powered unit attached to the patient. Power is enabled when the Patient Module is plugged into the DSP Card via the fiber optic cable, the host digital EEG computer is in the powered state and the Patient Module is enabled by pressing the control button located on the Patient Module. A five meter fiber optic cable is standard. The fiber optic cable provides outstanding patient isolation and flexible patient EEG Record Station placement options. A connector located at the top end of the module attaches to our standard e-Net headpiece or optionally a mini-jack for interface to standard cup electrodes. Self test, calibration and impedance tests are remotely activated from the host Digital EEG Machine through the DSP Interface Card or locally by depressing the appropriate test button on the Patient Module. A visual indication of control button function, system status and out of range impedance can be provided on the LCD display as controlled from the host Digital EEG Machine.

This submission report describes a device where EEG signal is acquired and presented to existing EEG recording and analysis equipment. The submission is not related to a machine learning system.

Here's an analysis based on the provided text, focusing on the requested information where applicable.

1. Table of Acceptance Criteria and Reported Device Performance

The submission form does not detail specific quantifiable acceptance criteria related to a machine learning model's performance (e.g., sensitivity, specificity, AUC). Instead, the device is a hardware system for acquiring and transmitting EEG signals. The performance evaluation focuses on the functional equivalence to a predicate device.

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

Not applicable. This device is a hardware system for signal acquisition and transmission, not a machine learning model that would typically have a test set of data for performance evaluation. The "test set" in this context would refer to internal validation and functional testing of the hardware. The documentation does not specify sample sizes for such tests or data provenance beyond general statements about system functionality.

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

Not applicable. The device's "ground truth" is its ability to accurately acquire and transmit EEG signals. This is typically verified through engineering tests and comparisons with predicate device performance, not by expert consensus on clinical data.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are used for establishing ground truth in clinical data for AI/ML performance evaluation. This device is a hardware system, and its functionality would be verified through technical specifications and performance testing.

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 not an AI-assisted diagnostic tool. Therefore, an MRMC study assessing human reader improvement with AI assistance would not be relevant.

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

Not applicable. This device is a hardware system, not an algorithm.

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

The "ground truth" for this device would be direct measurements and verification of its electrical signal acquisition and transmission capabilities against established engineering standards and the performance of the predicate device. This is primarily a hardware functionality and performance assessment, not one based on clinical data or expert consensus.

8. The sample size for the training set

Not applicable. As a hardware device, there is no "training set" in the context of machine learning.

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

Not applicable. There is no training set for a hardware device.

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The e-Net serves two functions as a part of the Hydro Dot NeuroMonitoring System. First, it locates the EEG electrodes according the 10-20 International System. Secondly, it conducts the electrical signal sensed by the electrodes from the skin to the EEG equipment.

The Medium e-Net is designed to fit patients with a head size in the range of 48 - 54 cm in circumference. It is constructed using the same materials and manufacturing processes as the larger sized net (54 - 62 cm) which is described in K930080. The e-Net serves two functions as a part of the Hydro Dot NeuroMonitoring System. First, it locates the EEG electrodes according the 10-20 International System. Secondly, it conducts the electrical signal sensed by the electrodes from the skin to the EEG equipment. The Medium e-Net is fabricated from the same materials as the larger version. Only the size of the components was reduced to achieve the proportional fit on the intended head sizes.

The provided text describes a 510(k) premarket notification for the Physiometrix, Inc. HydroSpot System, Model 1500 & 1501, and specifically the Medium e-Net Model 1210. However, the document does not contain information about acceptance criteria or a study that proves the device meets such criteria in terms of performance metrics like sensitivity, specificity, accuracy, etc. The document is primarily a regulatory approval letter based on substantial equivalence to a predicate device (K930080, HydroDot NeuroMonitoring System).

Here's a breakdown of why I cannot fulfill your request for performance data, and what information is available in the provided text:

Missing Information:

• Acceptance Criteria & Reported Device Performance: This document does not specify any quantitative performance metrics (like accuracy, sensitivity, specificity, etc.) nor their acceptance criteria. It primarily asserts that the device is "substantially equivalent" to a predicate device.
• Sample Size for Test Set & Data Provenance: No information on a test set, its size, or the provenance of the data is provided.
• Number of Experts & Qualifications for Ground Truth: No mention of experts or ground truth establishment for a test set.
• Adjudication Method: Not applicable as no test set evaluation is described.
• MRMC Comparative Effectiveness Study: There is no mention of a multi-reader multi-case comparative effectiveness study or any effect size related to human reader improvement with or without AI assistance. The device in question is a physical electrode net, not an AI algorithm.
• Standalone Performance Study: No standalone algorithmic performance study is mentioned.
• Type of Ground Truth Used: Not applicable, as no performance study is described.
• Sample Size for Training Set: No information on a training set is provided.
• How Ground Truth for Training Set was Established: Not applicable.

Information Available related to Substantial Equivalence:

The document states that the new device (Medium e-Net, Model 1210) is substantially equivalent to a predicate device (HydroDot NeuroMonitoring System, K930080). This determination is based on the following:

• Intended Use: "The e-Net serves two functions as a part of the Hydro Dot NeuroMonitoring System. First, it locates the EEG electrodes according the 10-20 International System. Secondly, it conducts the electrical signal sensed by the electrodes from the skin to the EEG equipment." This is consistent with its role as a "Cutaneous Electrode" for "EEG Monitoring."
• Design/Materials: "The Medium e-Net is fabricated from the same materials as the larger version [predicate device]. Only the size of the components was reduced to achieve the proportional fit on the intended head sizes."

Regarding "Acceptance Criteria" and "Study" and "Reported Device Performance":

In the context of this 510(k) submission, the "acceptance criteria" for the device to be cleared were based on demonstrating substantial equivalence to a previously cleared predicate device. The "study" that proves this is primarily a comparison of the new device's technological characteristics, materials, and intended use to the predicate device.

Since this device is a physical cutaneous electrode and not an AI/software device, the concepts of sensitivity, specificity, training sets, test sets, expert ground truth adjudication, and MRMC studies as typically applied to diagnostic AI are not relevant here. The substantial equivalence argument relies on the device being made of similar materials, having similar design principles (just scaled for different head sizes), and serving the same intended purpose as the predicate device. The 510(k) summary (K960873) explicitly compares the new device (Model 1210 Medium e-Net) to the predicate (K930080 HydroDot NeuroMonitoring System) based on physical attributes and intended function.

Therefore, I cannot populate the table or answer most of your questions as the provided text does not contain the type of performance study data you are asking for. It's a regulatory clearance document based on substantial equivalence, not a performance study report for a diagnostic algorithm.

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