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The SD LTM STIM is a low power, constant current, bi-phasic stimulator intended for cortical stimulation during electroencephalography examinations (i.e. stereoEEG).

The stimulation is applied to the brain using third-party stimulation cortical or intracranial electrodes) and the resulting cortical or deep brain potentials themselves are recorded using third-party cortical or intracranial electrodes.

The SD LTM STIM itself is an accessory to the SD LTM 64 EXPRESS Signal Acquisition System and SystemPlus EVOLUTION software. The stimulation parameters, the election and the activation of the stimulation current are all set-up and controlled from these devices. The SD LTM STIM can operate only when so connected and with the SystemPlus EVOLUTION software; it cannot serve as a stand-alone cortical stimulator.

The SD LTM is a low power, constant current, bi-phasic stimulator intended for cortical stimulation during electroencephalography examinations (i.e. stereoEEG). The stimulation is applied to the brain using third-party stimulation probes (including cortical or intraccanial electrodes) and the resulting cortical or deep brain potentials third-party cortical or intracranial electrodes. The use of these types of recordings can be related to the diagnosis/study of nervous system diseases.

Here's an analysis of the acceptance criteria and study information for the SD LTM STIM Cortical Stimulator, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document (K180761) is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than establishing new performance criteria through a de novo clinical trial. Therefore, explicit "acceptance criteria" related to a new clinical performance study, in terms of specific sensitivity, specificity, or other outcome measures, are not defined in the document.

Instead, the acceptance is based on demonstrating that the new device (SD LTM STIM) is as safe and effective as a legally marketed predicate device (Cervello Stim K151354). The "performance" reported is primarily a comparison of its technological characteristics to the predicate.

Here's a table summarizing the comparative technical performance, which serves as the basis for acceptance in this 510(k) submission:

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

The document explicitly states: "This premarket submission did not rely on the assessment of clinical performance data to demonstrate substantial equivalence."

Therefore, there was no test set of clinical data used in this submission. The evaluation was based on a comparison of technological characteristics and compliance with recognized voluntary consensus standards.

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

Since no clinical test set was used, no experts were involved in establishing ground truth for a test set.

4. Adjudication Method for the Test Set

As there was no clinical test set, no adjudication method was employed.

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

No MRMC comparative effectiveness study was done as the submission did not rely on clinical performance data. The device is a stimulator, not an diagnostic imaging AI, so this type of study would not typically be applicable for a 510(k) of this nature.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The device is a hardware cortical stimulator that works as an accessory to an EEG system and software. It is explicitly stated that "it cannot serve as a stand-alone cortical stimulator." Therefore, there was no standalone performance evaluation in the context of an algorithm. Its performance is always in conjunction with the controlling system.

7. The Type of Ground Truth Used

Given that no clinical performance data was used, no traditional "ground truth" (like pathology, expert consensus, or outcomes data) was established or utilized for comparison in this submission. The "ground truth" for demonstrating substantial equivalence was effectively the established performance and safety profile of the predicate device and compliance with relevant safety standards.

8. The Sample Size for the Training Set

There was no training set in the context of a machine learning or AI algorithm, as this is a hardware device submission.

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

Since there was no training set, this question is not applicable.

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