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The Cortical Stimulator is intended for use in functional brain mapping procedures during treatment of patients with seizure disorder, providing stimulation via electrode pairs or a hand held bipolar probe.

The Nicolet Cortical Stimulator supports clinician guided, intermittent electrical stimulation of the brain cortex to support diagnostic and surgical procedures. The cortical stimulation energy is applied through commercially available cortical electrodes (strip and grid electrodes) of a minimum 2.3 mm diametor and a Cardinal Health, NeuroCare stimulation probe. All stimulation is temporary and intermittent, applied in support of functional brain mapping procedures during treatment of patients with seizure disorder. The Nicolet Cortical Stimulator is a constant current stimulator. The stimulation current and frequency of stimulation is selected by the user. The Nicolet Cortical Stimulator (Cortical Stimulator) consists of two components: 1) Stimulus Switching Unit and 2) Cortical Stimulator Control Unit. The Cortical Stimulator Control Unit (CSCU) is powered by an external power supply. The CSCU provides the current stimulation source. The Stimulus Switching Unit (SSU) directs the stimulation to selected electrodes. The Cortical Stimulation Control Unit can be used in a stand-alone mode with stimulus provided through the Cardinal Health, NeuroCare stimulation probe. Alternatively the Cortical Stimulation Control Unit can be connected to the Stimulation Switching Unit. When the Stimulus Switching Unit is attached, the Stimulus Switching Unit interfaces between the Nicolet C64 Electroencephalography (EEG) amplifier and the amplifier's associated headbox. The headbox provides the interface between the amplificr and the brain contacting electrodes.

Here's an analysis of the provided text regarding the Nicolet Cortical Stimulator, focusing on acceptance criteria and the study data:

The provided text does not contain extensive information regarding specific acceptance criteria with numerical targets for device performance or a detailed comparative study proving the device meets said criteria. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices through a comparison of technological characteristics and confirmation of internal design control processes.

However, based on the information provided, we can infer some details and highlight what is not present.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present a formal table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or an effect size for human improvement. The closest information available is a comparison of technical features to predicate devices, which implicitly serves as the "acceptance criteria" in a substantial equivalence argument, where the new device should perform similarly or better within defined safety limits.

Here's a table summarizing the technical characteristics and their comparison to predicate devices, which substitutes for a direct "acceptance criteria" table in this type of submission:

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

• Sample Size: The document does not mention a "test set" in the context of clinical data for performance evaluation (e.g., patient studies). The testing referred to is "Software and hardware verification and validation" and "Safety standard compliance."
• Data Provenance: Not applicable, as there's no mention of a clinical test set with patient data. The "Data Summary" section refers to internal company testing and scientific literature, not a specific clinical study with patient data for this device submission.

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

This information is not provided. The submission does not detail any expert-adjudicated ground truth for a clinical test set.

4. Adjudication Method for the Test Set

Not applicable. No clinical test set or adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study is not mentioned or suggested by the provided text. This type of study is more common for diagnostic imaging devices where human interpretation is a key factor. The device is a stimulator, and its performance is evaluated based on technical specifications and safety, not on how human readers interact with its outputs in a diagnostic context.

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

This concept is not directly applicable to a cortical stimulator in the sense of an "algorithm" making a standalone decision. The device's "performance" is its ability to deliver specified electrical stimulation accurately and safely. The document states "Testing of the Nicolet System with the Cortical Stimulator was performed in compliance with the Cardinal Health, Inc. NeuroCare design control process. Testing included: 1. Software and hardware verification and validation, and 2. Safety standard compliance." This implies a form of standalone technical testing to ensure the device functions as intended without direct human intervention in the delivery of the stimulation, but not in terms of AI algorithm performance.

7. The Type of Ground Truth Used

The "ground truth" for this device, as inferred from the submission, is based on:

• Technical Specifications: The device's ability to precisely deliver current, frequency, and pulse width as specified.
• Safety Standards: Compliance with relevant electrical safety and medical device standards.
• Predicate Device Equivalence: The understanding that if the new device operates within the established safe and effective parameters of legally marketed predicate devices, it is considered safe and effective.

No specific pathology, outcomes data, or expert consensus on clinical cases is provided as "ground truth" for this device.

8. The Sample Size for the Training Set

Not applicable. The device is not an AI/ML algorithm that requires a "training set" of data. Its development involved traditional engineering design, verification, and validation processes.

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

Not applicable, as there is no training set for an AI/ML algorithm.

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