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This is an EEG electrode set intended for routine clinical settings where rapid placement of a large number of EEG electrodes is desired. Patient population from infants up to any age.

WaveGuard™ Net EEG cap consists of Saline-based EEG electrolyte- soaked sponge electrodes made of PU substrate plated with Ag and arranged in Chin-strap (placed under the chin). Cable bundle(s) with connector(s) The layout of the electrodes depends on the number of electrodes placed in the cap, and may be placed according to the international 10/10, 10/20 or 10/5 percent system, or according an equidistant "Duke" layout. Caps can have different sizes, ranging from baby to large adult head-size, spanning a total of 6 different cap sizes.

The provided text is a 510(k) Premarket Notification from the FDA for a medical device called the "WaveGuard Net EEG Cap." This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific performance acceptance criteria for an AI/algorithm-driven system.

The "acceptance criteria" and "study that proves the device meets the acceptance criteria" requested in the prompt are typically associated with performance validation studies for software or AI/ML-based medical devices, often requiring clinical or technical performance metrics against a defined ground truth.

This 510(k) summary primarily addresses:

• Device Description: What the device is (EEG electrode cap with saline-soaked electrodes).
• Intended Use: For routine clinical EEG settings.
• Comparison to Predicate Device: How it's similar and different from a previously cleared device (WaveGuard EEG Cap K110223). Key differences noted are electrode material (Coated Ag vs. Sintered Ag/AgCl), cap material (Silicone Mesh vs. Lycra), and Magnetic Resonance compatibility (MR unsafe vs. MR safe).
• Non-Clinical Testing: This section refers to performance standards and biocompatibility testing for the device itself (like electrical safety, material safety), not against an AI/algorithm's predictive accuracy or diagnostic performance. The listed standards (e.g., ISO 10993 series, 21 CFR PART 898) relate to safety, material characteristics, and electrical performance of electrodes/cables.

Therefore, based on the provided text, it is not possible to extract the information requested in the prompt, as the document does not describe a performance study for an AI/algorithm-driven device with accuracy, sensitivity, or specificity metrics.

Specifically, the document does not provide:

1. A table of acceptance criteria and reported device performance for an AI/algorithm. The document discusses performance standards for an electrode cap (e.g., electrical safety, biocompatibility), not a software's classification or prediction accuracy.
2. Sample size used for a test set or data provenance for an AI/algorithm validation.
3. Number of experts or their qualifications for establishing ground truth for an AI/algorithm test set.
4. Adjudication method for an AI/algorithm test set.
5. Information on any Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The device is a physical electrode cap, not an AI or software assistant for human readers.
6. Information on standalone (algorithm-only) performance. Again, this is a physical device.
7. The type of ground truth used in the context of an algorithm's performance. The "ground truth" for this device would relate to its physical and electrical properties meeting specified standards.
8. Training set sample size for an AI/algorithm.
9. How ground truth for the training set was established for an AI/algorithm.

The document's "Performance Data / Summary of Non-Clinical Testing" section refers to:

• 21 CFR PART 898—Performance Standard for Electrode Lead wires and Patient Cables: This regulation sets electrical safety and performance standards for the physical components of the EEG cap.
• Biocompatibility testing (ISO 10993 series): This ensures the materials in contact with the patient are safe (non-toxic, non-irritating, etc.).

These are standard regulatory requirements for all medical devices like EEG caps, not specific to the validation of AI/ML software.

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visor2 is used to aid 3D navigation for positioning of Transcranial Magnetic Stimulation (TMS) coils over preselected brain regions based on data from MRI measurements or scalp landmarks and optional EMG recording in clinical procedures. The visor2 system is compatible with the following commercially available TMS coils of the following TMS companies:

• · Mag & More: PMD70-aCool Double coil Figure-8 (510575)
• MagStim Company Ltd.: Double 70mm Remote Coil (3190-00 rev2), Double 70mm Air Film Coil (3910 00);
• MagVenture A/S: Double 75mm (C-B60), Double Cooled 75mm (Cool-B65), Double 95mm (D-B80), Angulated Double Cooled 97mm (Cool-B70 rev2), Butterfly figure-8 (C-B70)
• · Neuronetics Inc .: NeuroStar coil
  The visor2 system must be operated by a trained physician and must not be used during surgical operations.

The visor2 system is a 3D navigation system, supporting external/surface positioning of labeling identified commercially available Transcranial Magnetic Stimulation (TMS) coils based on the anatomical and functional areas for which use of the coil has been FDA cleared and as identified in cleared labeling for the coil. The visor2 system optionally records and processes electrophysiological signals to guide the user in the TMS coil placement. The visor2 system is compatible with TMS stimulators identified in the labeling that meet IEC 60601-1 and IEC 60601-1-2 electrical safety and EMC standards, a compatible optical tracking system, and further compatible accessories. Optionally, a compatible amplifier identified in the labeling may be applied for measuring electrophysiological signals. The visor2 system must be operated by a trained physician, and must not be used during surgical operations.
The visor2 system consists of a cart, an All-in-One medical computer with touch interface, a mounting plate for an optional amplifier applied for EMG recording and a Tracking camera with mounting arm. There are two fundamental visor2 system variants: single or dual display. the second configuration is available with an additional touch display added next to the All-in-One computer. Both variants are identical with regard to hardware features.

The acceptance criteria for the visor2 system and the study results are detailed in the provided text. The device is a 3D navigation system that aids in the positioning of Transcranial Magnetic Stimulation (TMS) coils over preselected brain regions based on MRI measurements or scalp landmarks, with optional EMG recording.

Here's a breakdown of the requested information:

1. A table of acceptance criteria and the reported device performance

The provided text combines the acceptance criteria (defined as "Main PASS criteria:" or implicitly as "Pass" alongside the test method and results) and the reported device performance within the "Bench testing demonstrated that the performance parameters of the visor2 system meets pre-specified requirements and are substantially equivalent to those of the predicate device" section.

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The neo monitor is an 8-channel electroencephalograph (EEG) acquisition software. The device is intended to record and display EEG and aEEG signals for monitoring the brain status of neonatal patients (defined as from birth to 28 days postdelivery, and corresponding to a postconceptual age of 24 to 46 weeks). The device is to be used in a hospital environment by qualified clinical practitioners. The device does not provide any diagnostic conclusion about the patient's condition to the user.

The nëo™ system is a reduced montage neonatal electroencephalograph device that acquires, displays, stores, and archives electroencephalographic signals from the brain. By application of electrodes at specific location on the cranium,using up to 10 surface electrodes placed at specific locations the system functions to measure and record electrical activity of the brain by acquisition of electroencephalograph data and amplitude-integrated electroencephalograph (electroencephalograph signals that have been filtered and displayed in a specific manner).

The neo System is an electromedical device incorporating software. The device itself has no patient contact, but is intended for use with FDA-cleared ECG electrodes. nëo™ Monitor software, which, when installed into a compatible touchscreen PC and paired with a physiological signal amplifier (eego model EE-411) forms the nëo™ Monitor System. The system is a compact and easy-to-use and can be set on a bedside table, pole-mounted, or on a cart in the neonatal care areas.

The system, as-delivered to the customer, is comprised of:

• nëo Monitor software (pre-installed)
• 15" Panel PC
• eego EE-411 model amplifier
• Mounting plate
• nëo User Manual .

Here's a breakdown of the acceptance criteria and study information for the nëo Monitor System, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting specific performance metrics against pre-defined acceptance criteria for the entire device. Instead, it compares the technological characteristics of the nëo Monitor System to its predicate, the Olympic Brainz Monitor.

The table below summarizes the technical specifications compared, which implicitly serve as acceptance criteria for functionality. It also includes the performance for the burst suppression detection software, which is a key differentiating feature with its own implicit performance.

2. Sample Size Used for the Test Set and Data Provenance

The provided 510(k) summary does not detail a clinical study with a specific test set, sample size, or data provenance (e.g., country of origin, retrospective/prospective data for clinical validation). The submission relies on non-clinical testing against recognized standards and a comparison of technological characteristics to a predicate device.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Since a clinical test set for performance validation (beyond technical specifications) is not described, there is no information on:

• The number of experts used.
• The qualifications of those experts.

4. Adjudication Method for the Test Set

As no specific clinical test set for performance evaluation is described, there is no information on any adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No MRMC comparative effectiveness study is mentioned in the provided document. The focus is on demonstrating substantial equivalence based on technical specifications and non-clinical testing, not on human reader performance with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

For the specific functions like aEEG signal processing and burst suppression detection, the document describes the algorithms and their technical specifications (e.g., aEEG filter specification, presence of BSR/IBI). The fact that these are listed as device features implies standalone algorithm functionality. However, a formal "standalone performance study" with specific metrics (e.g., sensitivity, specificity for burst detection against a human-annotated ground truth) is not detailed in this document. The comparison is largely at a functional and technical specification level.

7. The Type of Ground Truth Used

For the technical specifications, the ground truth is implicitly defined by:

• Engineering specifications and test methodologies for electrical performance (e.g., sampling rate, resolution, noise, impedance).
• Standardized test signals (e.g., sine wave inputs for aEEG filter specification).
• Reference standards like IEC 60601-1, IEC 60601-2-26, etc. for safety and essential performance.

There is no mention of expert consensus, pathology, or outcomes data being used as ground truth for a clinical validation study in this summary.

8. The Sample Size for the Training Set

The document does not mention any training set size as it concerns hardware/software specifications and regulatory compliance rather than an AI/machine learning model whose performance on classification/detection tasks is being clinically validated against a dataset. While the burst suppression detection software might involve algorithms, the document doesn't delve into its development data.

9. How the Ground Truth for the Training Set Was Established

Since no training set is described, no information is provided on how its ground truth might have been established.

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The eego amplifier is intended to be used by or under the direction for acquisition of EEG signals and to transmit them digitally to a computer. The device is intended for use on humans. The device is intended for use in a clinical environment (e.g., hospital, physician's office, etc). The device is not intended for use in life support systems.

The eego amplifier is a multi-channel, plastic enclosed EEG amplifier. There are two ranges for the variations of amplifiers (models):

• 1. EE-2xx, and
• 2. EE-4xx.
     In total, there are 8 models. The EE-411 and EE-430 amplifiers are built using identical components and share identical internal structure, functionality and performance. The eego amplifier electroencephalography (EEG) input is from commercially available EEG electrodes from the Waveguard™ EEG Cap cleared in K110223. The eego amplifiers support EEG acquisition and storage by:
• 1. referential DC input channels,
• 2. parallel trigger input channel.
     Electrode Impedance values can be measured for all referential electrodes, reference and patient ground electrode. Signal sampling rate and gain are set through the user interface of the Software Development Kit (SDK). The EE-2XX series of eego amplifier is powered by a built-in, rechargeable battery. The battery provides a minimum of 4 hours of operation time. The amplifier can be applied for data acquisition with the 12 V battery charger source applied. The USB port supports interface to the computer running the SDK. The EE-4XX series of eego amplifier is powered by the USB port connection to a computer.

This document describes the premarket notification for the eego amplifiers, focusing on its substantial equivalence to a predicate device rather than a study proving the device meets specific acceptance criteria for a diagnostic algorithm.

Therefore, many of the requested points regarding diagnostic performance, ground truth, sample sizes for training/test sets, expert adjudication, and comparative effectiveness with AI assistance cannot be extracted Directly from the provided text. The document primarily concerns the safety and performance of a medical device (EEG amplifier) and its compliance with general medical device standards.

Here's an attempt to answer the questions based on the available information:

1. A table of acceptance criteria and the reported device performance

The document does not provide specific acceptance criteria or quantitative performance metrics for a diagnostic task. Instead, it refers to compliance with international safety and performance standards for medical electrical equipment.

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

This information is not provided in the document. The document describes performance testing for compliance with technical standards, not a diagnostic study involving patient data.

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

This information is not provided. Ground truth in the context of diagnostic algorithms is not relevant to this type of device submission, which focuses on hardware performance and safety standards.

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

This information is not provided as it is not applicable to the type of device and testing described.

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

No, an MRMC comparative effectiveness study was not done. The eego amplifier is a device for signal acquisition, not an AI-powered diagnostic algorithm.

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

No, a standalone algorithm performance study was not done. The device itself is not an algorithm, but hardware for signal acquisition.

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

This information is not provided in the context of diagnostic accuracy. The "ground truth" for the performance testing mentioned would be the established specifications and requirements of the referenced IEC standards.

8. The sample size for the training set

This information is not provided. Training sets are relevant for machine learning algorithms, which is not the primary subject of this submission.

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

This information is not provided.

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This is an EEG electrode set intended for routine clinical settings where rapid placement of large number or EEG electrodes is desired.

WaveGuard caps consist of Cap fabric Electrodes arranged in a certain layout Chin-strap (placed under the chin) Cable bundle(s) with connector(s) The layout of the electrodes depends on the number of electrodes placed in the cap, and may be placed according to the international 10/20, 10/10 or 10/5 percent system, or according an equidistant "Duke" layout. Caps can have different sizes, ranging from neonatal size (23-weeks old) to large adult head-size, spanning a total of 11 different cap sizes.

The provided 510(k) summary for the WaveGuard™ EEG cap focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing and a feasibility study, rather than establishing specific performance acceptance criteria for the device itself.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, and ground truth types cannot be fully extracted as it's not present in the document.

Summary of available information regarding performance:

1. Table of acceptance criteria and the reported device performance

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

• Sample Size: Not explicitly stated, but the phrase "beginning from the very first baby recorded" suggests a small initial sample for the feasibility study. The exact number of babies or recordings is not provided.
• Data Provenance: Not explicitly stated, but implied to be from a clinical setting where data on "extremely preterm babies" was collected, likely in a prospective manner given it's a "feasibility study." Country of origin is not mentioned.

3. 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)

• The document does not specify the number or qualifications of experts used to establish ground truth for the clinical investigation. The "successful recording" outcome suggests expert interpretation of EEG data was involved, but details are absent.

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

• The document does not describe any adjudication method for the clinical investigation.

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

• No MRMC comparative effectiveness study was conducted. The device is an EEG electrode cap, not an AI-powered diagnostic tool, so comparison of human reader improvement with or without AI assistance is not applicable.

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

• Not applicable. The WaveGuard™ EEG cap is a hardware device for signal acquisition, not an algorithm. Therefore, a standalone algorithm performance study was not performed.

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

• For the clinical investigation, the "ground truth" seems to be the successful acquisition of high-fidelity EEG recordings as determined by clinical assessment and interpretation. This would inherently involve expert judgment on the quality and diagnostic utility of the EEG data.

8. The sample size for the training set

• Not applicable. This device is a hardware component (EEG cap) and does not involve machine learning algorithms that require a training set.

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

• Not applicable. As stated above, there is no training set for this device.

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The software is intended for use by a trained/qualified MEG technologist or physician on both adult and pediatric subjects for the visualization of human brain function by fusing a variety of magnetoencephalographic (MEG) and electroencephalographic (EEG) information with rendered images of either an idealized head model and an idealized magnetic resonance (MR) image or a subject's MR image based head model and the subject's MR images

The MaxInsight ™ software is a software-only product. It runs on a personal computer and requires no specialized hardware. It displays digitized MEG signals, topographic maps, etc. It displays electrical brain activity correlated to anatomical brain information, which is provided by means of either an idealized head model and an idealized MRI image or a subject's MRI image based head model and the subject's MRI images. These functions are all controlled and interpreted by the user. The MaxInsight software imports digital MEG and co-registered EEG data (in the Neuromag Functional Image File Format) and permits its visualization. The digitized MEG/EEG input is read from a file on the personal computer (or available across the network). The software also provides a report generator to transfer relevant information, such as patient and recording information, event information, topographic images and images of brain activity, to a printable document.

The provided text does not contain detailed information about the acceptance criteria and a specific study proving the device meets these criteria in the format requested. The document is a 510(k) summary for the MaxInsight™ Electroencephalograph Software, focusing primarily on demonstrating substantial equivalence to a predicate device.

Here's a breakdown of what is available and what is missing:

The document states:

• "Predetermined software development processes were employed in the design and testing of this software product, and validation testing was performed to ensure compliance with the product specifications."
• "The tests demonstrated that the device is as safe, as effective, and performs in a substantially equivalent manner to the predicate device."
• "After analyzing both bench and clinical testing data, it is the precedes as not relies and Jang Solutions GmbH that the MaxInsight™ software is as safe and effective as the predicate devices, has few technological differences, and has no new indication for use, thus rendering it substantially equivalent to the data analysis package of the predicate devices."

However, these statements are general affirmations and do not provide the specific details requested in your prompt.

Missing Information:

• Specific Acceptance Criteria and Reported Device Performance Table: The document does not provide a table outlining specific performance metrics (e.g., accuracy, sensitivity, specificity, computational speed) that the device was tested against, nor does it report the device's performance results quantitatively.
• Sample Size for Test Set and Data Provenance: No information is given about the number of cases or patients used in any "clinical testing data" or "validation testing," nor their origin (country, retrospective/prospective).
• Number and Qualifications of Experts for Ground Truth: There is no mention of experts used to establish ground truth for a test set.
• Adjudication Method: No adjudication method is described.
• Multi-reader Multi-case (MRMC) Comparative Effectiveness Study: There is no indication that an MRMC study was performed, nor any effect size regarding human reader improvement with AI assistance.
• Standalone Performance Study: While the device is software-only, "standalone performance" in the context of diagnostic accuracy metrics is not reported. The focus is on its function as a visualization tool; its "performance" is implicitly tied to its ability to display signals and images accurately as intended.
• Type of Ground Truth: The document does not specify the type of ground truth used for any testing (e.g., expert consensus, pathology, outcomes data).
• Sample Size for Training Set: Since this is not an AI/ML device in the modern sense (it's visualization software), there isn't a "training set" as conceptualized for machine learning algorithms.
• How Ground Truth for Training Set was Established: Not applicable given the nature of the device.

In summary, the provided 510(k) summary for K070358 focuses on demonstrating substantial equivalence of a medical imaging visualization software to a predicate device through general statements about software development processes and "bench and clinical testing data." It does not provide the detailed performance metrics, study designs, or ground truth methodologies typically found in submissions for devices that make diagnostic claims based on algorithmic analysis or where quantitative performance against a specific standard is required to prove effectiveness.

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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

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