(174 days)
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.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Functional equivalence to predicate device (K930080, HydroDot NeuroMonitoring System) | The modified system uses the same electrodes and headpiece for signal acquisition but has changes in the modules used to transmit signals. The Patient Module and DSP Interface Card (replacing Monitor Module) perform similar functions as the predicate device's components for signal transmission and interfacing with EEG recording equipment. The system aims to acquire and present EEG signals according to the 10-20 International System, identical to the predicate device's stated function. |
| Elimination of AC power supply and EEG Adapter Cable while maintaining functionality | The AC power supply and EEG Adapter Cable have been eliminated, implying the new system achieves intended functionality without these components. |
| Outstanding patient isolation and flexible patient EEG Record Station placement | Provided by a five-meter fiber optic cable. |
| Remote and local activation of self-test, calibration, and impedance tests | 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. |
| Visual indication of control button function, system status, and out-of-range impedance | Provided on the LCD display as controlled from the host Digital EEG Machine. |
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.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).