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510(k) Data Aggregation
(69 days)
Intended to record and display EEG and EP (evoked potential) data in the clinic and hospital. It is intended to aid in the diagnosis and monitoring of potential disorders of the central and peripheral nervous system.
Cognitrace provides a comprehensive EEG/ERP Data acquisition and analysis System. Cognitrace consists of the following components: 1 EEG/ERP amplifier, Power supply for EEG/ERP amplifier, eemagine EEG (K002631) software with Cognitrace extension modules, Stimulation unit. Cognitrace can perform complete sets of measurements for neurology and psychiatry: EEG, Visual Evoked Potential (VEP), Auditory Evoked Potential (AEP), P300 (Auditory oddball paradigm), Visual P300 (Visual oddball paradigm), CNV (Contingent Negative Variation), Option to add protocols using acoustic or visual stimulation. The software components are: eemagine EEG, a novel, user-friendly and efficient software package for the analysis of EEG data and eego, a novel, user-friendly EEG data acquisition system and allows you to record and review EEG, offering different amplifier configurations, eego is seemlessly integrated with the eemagine EEG software, which offers simplicity and efficiency in the analysis of EEG studies.
The provided text describes a 510(k) submission for the Cognitrace device, which is an electroencephalograph with evoked response stimulation. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed device performance metrics in the way an AI/ML device would.
Therefore, many of the requested elements for an AI/ML device study (like sample size for test/training sets, ground truth establishment details, multi-reader multi-case studies, etc.) are not applicable and not present in this document.
However, I can extract the information that is available and present the "acceptance criteria" as implied by the substantial equivalence comparison to the predicate device.
Acceptance Criteria and Device Performance for Cognitrace (Based on Substantial Equivalence)
The Cognitrace device demonstrates its performance by showing substantial equivalence to a legally marketed predicate device, the Nicolet Bravo Multi Modality System (K991054). The "acceptance criteria" are implicitly defined by meeting or exceeding the specifications and functionalities of the predicate device, with a focus on safety and effectiveness.
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Acceptance Criterion (from Predicate) | Cognitrace Performance (as Reported) | Notes/Comparison |
|---|---|---|
| Indications For UseRecord and display EEG, EP, EMG, TCD; aid in diagnosis/monitoring of central/peripheral nervous system and muscles. | Record and display EEG and EP (evoked potential) data; aid in diagnosis and monitoring of potential disorders of the central and peripheral nervous system. | Cognitrace has a slightly narrower stated indication (no EMG or TCD mentioned in the table), but it still addresses the core EEG and EP functions. The 510(k) conclusion explicitly states "no new indications for use". |
| Prescription Use | Yes | Same as predicate. |
| Testing ModesEEG, EP, EMG | EEG, EP | Cognitrace supports EEG and EP. EMG is not listed as a supported mode for Cognitrace in the table. |
| Head BoxUses a headbox; fewer channels (32) | Various headboxes available for up to 128 channels | Cognitrace offers more channels than the predicate. |
| Filters - Low FilterFirst order IIR digital filter | Inside amplifier: Highpass: none | Different processing approach for low-frequency filtering. |
| Filters - High FilterSecond order IIR digital filter | Inside amplifier: Lowpass digital FIR filter, cutoff frequency = 0.27 * sample frequency | Different processing approach for high-frequency filtering. |
| Sample Frequencies< 1000 Hz | 256 Hz, 512 Hz, 1024 Hz, 2048 Hz | Cognitrace supports higher maximum sampling frequencies. |
| Noise (0.1 - 100 Hz)< 2 μV (p-p), (0.7 µVrms) | EEG, BIP inputs: noise < 1.0 µVrmsAUX inputs: Noise < 20 µVrms | Cognitrace's EEG noise specification (1.0 µVrms) is comparable to or slightly higher than the predicate's 0.7 µVrms. |
| Software - Data AnalysisAveraging, Signal Processing, Filtering, Artifact detection, Baseline correction | Averaging, Dipole Fit, Spike Detection, Signal Processing, Filtering, Artifact detection, Baseline correction, Grand averaging, Detrending, Resampling, Spike detector, Dipole Fit, FFT, Coherence, Surface Laplacian | Cognitrace offers a significantly expanded set of data analysis features compared to the predicate. |
| Software - Data Acquisition | Montage, EEG signal acquisition, Writing of EEG data to file | Explicitly listed for Cognitrace. |
| Software - Visualization | HTML templates, Screen calibration, EEG view to display EEG traces, Reports and wizards | More detailed visualization and reporting features listed for Cognitrace. |
| Overall Safety and Effectiveness | Demonstrated through bench, electrical safety, EMC, and user testing | Explicit conclusion of equivalence. |
2. Sample Size for the Test Set and Data Provenance
- Not Applicable / Not Provided. This document does not describe a clinical study with a specific test set for algorithm performance. The basis for equivalence is primarily technical comparison, bench testing, electrical safety (EMC), and general "user testing." No details on the number of users or the nature of this testing are provided. Data provenance (e.g., country of origin, retrospective/prospective) is also not applicable in this context.
3. Number of Experts used to Establish Ground Truth for the Test Set and Qualifications
- Not Applicable / Not Provided. As there's no specific algorithm performance study described, there is no mention of experts establishing a ground truth for a test set.
4. Adjudication Method for the Test Set
- Not Applicable / Not Provided. No test set or ground truth adjudication method is described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No. No MRMC study or any form of comparative effectiveness study with human readers (with vs. without AI assistance) is mentioned.
6. Standalone (Algorithm Only) Performance Study
- Not Applicable / Not Provided. This document describes a medical device system (hardware and software) intended for use by clinicians, not a standalone AI algorithm. The performance evaluation focuses on the entire system's functionality and safety relative to the predicate.
7. Type of Ground Truth Used
- Not Applicable / Not Provided. For a substantial equivalence claim based on technical specifications, bench testing, and general user testing of an EEG/EP device, a "ground truth" as you would define for an AI algorithm (e.g., pathology, outcomes data) is not directly applicable or discussed. The "ground truth" for the equivalence claim is the established performance and safety of the legally marketed predicate device.
8. Sample Size for the Training Set
- Not Applicable / Not Provided. This device is not described as an AI/ML algorithm that undergoes "training."
9. How the Ground Truth for the Training Set was Established
- Not Applicable / Not Provided. As above, no training set or its associated ground truth is discussed.
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