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ENOBIO is an EEG portable monitoring device of 8, 20, 32 channels intended for the use in clinical patient monitoring for use in hospitals and other medical environments.

The Enobio is intended to acquire, store, transmit and display electrophysiological signals in wireless mode as an aid in diagnostics. The system digitizes analogue EEG signals collected by a cap with electrodes, amplifies them, and uses WiFi connectivity to transmit the EEG data to a dedicated host computer with the software.

This device is intended to be used by trained health-care personnel. It is restricted to sale by or on order of a physician.

The Neuroelectrics device, Enobio Wireless EEG, performs electroencephalographic measurements with non-invasive technology.

Enobio is a wireless, battery-operated and portable electrophysiology sensor system for the recording of the electroencephalogram (EEG). It has been designed for use in a clinical environment or hospital.. Enobio use must be controlled by specialized medical personnel able to guarantee the correct recording.

ENOBIO WIRELESS EEG, consists of three models designated as the 8, 20 and 32 channels. ENOBIO WIRELESS EEG has a sampling rate of 500 SPS, it works in a bandwidth from 0 to 125Hz (DC coupled) and it provides a resolution of 24 bits - 0.05μV. Enobio is capable of acquiring a variety of electrophysiological signals. These signals include EEG, EOG, ECG and EMG.

The Enobio system is primarily made up of the following three items:

• Necbox (amplifier, transmitter and control box),
• the neoprene cap with the electrodes and
• the NIC software.

The Necbox, which has a data amplifier, connects directly via Wi-Fi to the Neuroelectrics Instrument Controller (NIC) software running on a computer. The EEG data is streamed through the standard Wi-Fi band, and the standard Wi-Fi operating distance range is 10 meters. In all situations the amplifiers store a copy of the data locally to allow for data back-up. This amplifier provides storage and subsequent transmission of data that is not transferred live when the amplifier is in out of range situations.

The provided document is a 510(k) summary for the Enobio Wireless EEG device. It does not present a study proving the device meets acceptance criteria in the typical sense of a clinical trial or performance study with detailed metrics against defined acceptance criteria. Instead, it focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance testing and a comparison of characteristics.

Therefore, many of the requested sections about acceptance criteria, sample sizes, ground truth, expert adjudication, and comparative effectiveness studies are not applicable directly from this document, as the submission strategy relies on non-clinical data and equivalence.

Here's a breakdown of the available information:

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

The document doesn't provide a table of acceptance criteria with quantitative performance metrics against them. Instead, it states that the device "passed all the testing in accordance with internal requirements, national standards, and international standards" and lists the types of tests conducted. These tests implicitly serve as the performance evaluation.

• ISO 10993-1: 2009 (Biological Evaluation of Medical Devices Part 1: Evaluation and Testing)
• ISO 10993-5: 2009 (Test for In Vitro Cytotoxicity)
• ISO 10993-10: 2010 (Tests for Irritation and Delayed-Type Hypersensitivity) |
  | Electrical Performance | Passed testing in accordance with:
• IEC 60601-1: 2005 + CORR. 1 (2006) + CORR 2 (2007) (General Requirements for Basic Safety and Essential Performance)
• IEC 60601-1-2:2007 (Third Edition) (EMC Requirements and Tests)
• IEC 60601-2-26:2012 (Particular requirements for electroencephalographs)
• IEC 60601-1-11: 2010 (Requirements for Basic Safety and Essential Performance - Collateral Standard: Electromagnetic Compatibility)
• IEC 61000-4-3 (Radiated, radio-frequency, electromagnetic field immunity test) |
  | Software V&V | Testing conducted in accordance with IEC 62304 and FDA Guidance |
  | Disinfection | Testing completed |
  | Electrode Compatibility | Testing completed |
  | Product Specification Compliance Tests | Passed for:
• Bandwidth
• Sampling Rate (Fixed 500 Hz, within predicate's range)
• Input Referred Noise
• Dynamic Range
• Operating Hours
• Resolution (24 bits - 0.05μV)
• Input Impedance |

2. Sample sized used for the test set and the data provenance

Not applicable for non-clinical performance testing of a hardware device. The tests were in a lab setting, not on patient data.

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

Not applicable. The "ground truth" for non-clinical hardware performance testing is established by engineering specifications and national/international standards, not by expert medical interpretation.

4. Adjudication method

Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The submission explicitly states: "There was no human clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."

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

The device is an EEG acquisition system, not an AI algorithm. Its performance is evaluated based on its ability to accurately acquire and transmit electrophysiological signals, which were tested via non-clinical performance tests.

7. The type of ground truth used

For the non-clinical performance data, the "ground truth" is defined by the technical specifications and accepted international standards for medical electrical equipment and electroencephalographs (e.g., IEC 60601 series, ISO 10993 series). Performance is measured against these established technical benchmarks.

8. The sample size for the training set

Not applicable. This is a hardware device; no "training set" in the context of machine learning is involved.

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

Not applicable.

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