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The Elekta Neuromag® with MaxFilter 2.1 is intended for use as a magnetoencephalographic (MEG) device which non-invasively detects and displays biomagnetic signals produced by electrically active nerve tissue in the brain. When interpreted by a trained clinician, the data enhances the diagnostic capability by providing useful information about the location relative to brain anatomy of active nerve tissue responsible for critical brain functions.

Elekta Neuromag® with MaxFilter™ non-invasively measures the magnetoencephalographic (MEG) signals (and, optionally, electroencephalographic (EEG) signals) produced by electrically active tissue of the brain. These signals are recorded by a computerized data acquisition system, displayed and may then be interpreted by trained physicians to help localize these active areas. The locations may then be correlated with anatomical information of the brain. MEG is routinely used to identify the locations of visual, auditory, somatosensory, and motor cortex in the brain when used in conjunction with evoked response averaging devices. MEG is also used to non-invasively locate regions of epileptic activity within the brain. The localization information provided by MEG may be used, in conjunction with other diagnostic data, in neurosurgical planning.

This premarket notification represents modifications made to our current product. The present device differs from the predicate device, K050035, Elekta Neuromag® with Maxwell Filter only in the following areas of functionality: Spatiotemporal interference elimination, Graphical user interface; and Offline averager. The modification also adds compatibility with internal active shielding, an interference removal method described in K081430. MaxFilter™ is intended to be used with Elekta Neuromag® MEG products in reducing measurement artifacts.

Here's a breakdown of the acceptance criteria and study information for the Elekta Neuromag® with MaxFilter 2.1, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The 510(k) summary focuses on demonstrating substantial equivalence to the predicate device, K050035. The "acceptance criteria" for the new features are primarily that they provide enhanced functionality without negatively impacting the existing performance, and for the core function of MEG measurement, the new device maintains accuracy substantially equivalent to the predicate.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "clinical study" but does not detail the number of subjects or cases.
• Data Provenance: Not explicitly stated. Given that Elekta Oy is based in Helsinki, Finland, and the 510(k) is for the US FDA, the clinical study could involve data from various countries. The document does not specify if it was retrospective or prospective.

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

• This information is not provided in the summary. The "clinical study" is mentioned for measurement accuracy with non-moving heads, but details about ground truth establishment by experts for localization or diagnostic capabilities are absent. The intended use states data "may then be interpreted by trained physicians," implying expert interpretation, but doesn't specify how ground truth for the study was established.

4. Adjudication Method for the Test Set

• This information is not provided in the summary.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• A MRMC study comparing human readers with and without AI assistance is not mentioned in the summary. The study focuses on the device's technical performance and substantial equivalence.

6. Standalone (Algorithm Only) Performance Study

• A standalone performance study was implicitly done for the technical accuracy of the MaxFilter 2.1 algorithm. The document states "Performance testing consisted of software validation, phantom testing and clinical testing." The "within 2 mm accuracy of the source in a phantom" refers to the algorithm's performance in a controlled environment. However, this is not a "standalone performance" in terms of diagnostic effectiveness without human interpretation, as the device is intended for use by trained clinicians.

7. Type of Ground Truth Used

• For phantom testing: The ground truth would be the known, precisely controlled source location within the phantom, allowing for direct comparison of the device's localization output to this known truth.
• For clinical testing: The document states "provided substantially equivalent measurement accuracy in a clinical study with non-moving heads." This suggests that the ground truth for "measurement accuracy" in a clinical setting likely referred to established and accepted methods for assessing MEG signal quality and source localization, potentially compared against the predicate device's output or other established neurophysiological markers. However, specific details of how this clinical ground truth was established are not provided. It is not explicitly stated if pathology, expert consensus on clinical finding, or outcomes data were used as ground truth for clinical diagnostic performance.

8. Sample Size for the Training Set

• The document does not explicitly mention a training set or its sample size. MaxFilter 2.1 is described as a modification to an existing product (K050035) with new algorithms (tSSS). The development of these algorithms would involve theoretical work and potentially internal data sets for optimization, but these are not referred to as a "training set" in a machine learning context within this 510(k) summary.

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

• As a training set is not explicitly referred to, the method for establishing its ground truth is not provided. The development of the tSSS algorithm would rely on established physics and signal processing principles for MEG data, rather than a "ground truth" derived from patient data in the way a machine learning model would.

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