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The REMI Remote EEG Monitoring System is indicated for use in healthcare settings where near real-time and/or remote EEG is warranted and in ambulatory settings where remote EEG is warranted. REMI System uses single patient, disposable, wearable sensors intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for a duration up to 30 days.

REMI System uses the REMI Mobile software application that runs on qualified commercial off-the-shelf mobile computing platforms. REMI Mobile displays user setup information to trained medical professionals and provides notifications to medical professionals and ambulatory users. REMI Mobile receives and transmits data from connected REMI Sensors to the secure REMI Cloud where it is stored and prepared for review on qualified EEG viewing software.

REMI System does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. REMI System is indicated for use with adult and pediatric patients (1+ years).

The REMI System has three major components:

• 1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
• 2. REMI Mobile A mobile medical application that is designed to run on a qualified commercial-off-the-shelf mobile computing platform (an Android tablet for use in healthcare settings, and a portable/wearable Android device (phone or smartwatch) for use in ambulatory settings), acquire EEG data transmitted from REMI Sensors and then transmit the EEG data and associated patient information via wireless encrypted transmission to.
• 3. REMI Cloud A HIPAA-compliant secure cloud storage and data processing platform where data is processed into a qualified EEG reviewing software format for neurological review.

The provided document is a 510(k) Pre-market Notification Summary for the REMI Remote EEG Monitoring System (K243185). This document details the device's characteristics, indications for use, and the studies conducted to demonstrate its substantial equivalence to a predicate device (REMI Remote EEG Monitoring System, K230933).

Based on the provided information, here's a description of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily relies on comparisons to its own predicate device (K230933) and general performance testing against recognized standards. Specific quantitative acceptance criteria are not explicitly detailed in a table format within this summary, but the general assertion is that the device met all predetermined acceptance criteria derived from the listed tests.

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The Natus BrainWatch System including the Natus BrainWatch Headband, is intended to record and store EEG signals and present these signals visually to assist trained medical staff in making neurological diagnoses in patients aged 2 years and older.

The device does not provide any diagnostic conclusions about the subject's condition and does not provide any automated alerts of an adverse clinical event. The Natus BrainWatch System is intended for use within a professional healthcare facility or clinical research environment. The Natus BrainWatch Headband is intended for single-patient use.

The Natus BrainWatch system is a reliable, mobile, and easy-to-use EEG device intended to record, store, and visually present EEG signals to assist trained medical staff in making neurological diagnoses in patients.

The system includes a touchscreen tablet as its primary interface. The Natus BrainWatch Headband is a single-use disposable headpiece with an integrated array of 10 passive electrodes that are applied to the patient's head to record EEG signals when connected to an amplifier.

The Natus BrainWatch System consists of the following components: Tablet, IV Pole Handle, Amplifier, Headband(s), Gel Pods and a Mobile Application:

• Touchscreen Tablet with charger
• Single-Use disposable elastic fabric headband with 10 electrodes (available in sizes Small, Medium, and Large) containing:
  • Hydroflex patch with 2 built-in electrodes
  • 8 electrodes attached to gel pods used to improve impedance levels labeled L1-L4, R1-R4
• Wireless amplifier that attaches to the headband and connects to the tablet via Bluetooth
• IV pole handle that holds the tablet for a hands-free experience
• Gels pods attach to the electrodes to improve impedance levels

The Natus BrainWatch System is a portable 10-channel EEG monitoring system. 10 patient electrodes are used to record the 10 channels. Channels 1-5 should be used for the patient's left hemisphere, with channel 1 at the front of the patient's head and channel 5 at the back. Channels 6-10 should be used for the patient's right hemisphere, with channel 6 at the front of the patient's head and channel 10 at the back.

EEG recording files are transferred wirelessly to a computer from the Natus BrainWatch Tablet using a Wi-Fi connection. The EEG sessions from Natus BrainWatch are stored using a cloudbased solution which allows the end user to view studies at a later date. Recorded sessions can be reviewed remotely on a computer using the Neuroworks EEG software.

The device is a portable, 10-channel EEG monitoring system. The device connects to a headband consisting of 10 patient electrodes which are used to form the 10 channels and may be used with any scalp EEG electrodes.

The system acquires the EEG signals of a patient and presents the EEG signals in visual formats in real time. The EEG recordings are displayed on a computer or tablet using an EEG viewer software. The visual signals assist trained medical staff to make neurological diagnoses. It does not provide any diagnostic conclusion about the subject's condition and does not provide any automated alerts of an adverse clinical event.

Micro-USB cable is used to connect the Natus BrainWatch System to power adapter for charging. Bluetooth is used to connect amplifier with tablet to transfer EEG recording files. When the Natus BrainWatch System is connected to a power adapter of a computer, all EEG acquisition functions are automatically disabled.

The Natus BrainWatch System is a portable 10-channel EEG monitoring system intended to record, store, and visually present EEG signals to assist trained medical staff in making neurological diagnoses in patients aged 2 years and older. It does not provide diagnostic conclusions or automated alerts of adverse clinical events.

Here's a breakdown of the acceptance criteria and the study verifying the device's performance:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text details the performance verification against various standards rather than specific quantitative acceptance criteria for clinical performance. The focus is on demonstrating safety and electrical performance equivalence to predicate devices.

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

The document does not specify a separate "test set" in the context of clinical data for algorithmic performance. The testing described is primarily bench and engineering verification against international standards. Therefore, information about sample size, country of origin, or retrospective/prospective nature of a clinical test set is not provided.

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

Not applicable, as the provided information does not describe a study involving clinical ground truth establishment by experts for a test set. The device assists trained medical staff in making diagnoses but does not provide diagnostic conclusions itself.

4. Adjudication method for the test set

Not applicable, as there is no described clinical study involving a test set and ground truth adjudication.

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 is described. The device is a data acquisition and visualization tool; it does not explicitly feature AI for interpretation or diagnostic assistance.

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

Not applicable. The "Natus BrainWatch System" is an electroencephalograph (EEG) device that records and visually presents signals for trained medical staff to interpret. It does not provide any diagnostic conclusions or automated alerts, meaning it is not a standalone diagnostic algorithm.

7. The type of ground truth used

Not applicable. The testing focuses on engineering performance, safety, and functional compliance rather than diagnostic accuracy against a clinical ground truth (e.g., pathology, outcomes data, or expert consensus).

8. The sample size for the training set

Not applicable, as the document does not describe the development or validation of an AI algorithm with a training set. The device's function is to capture and display raw EEG signals.

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

Not applicable, as there is no described training set or AI algorithm for which ground truth would need to be established.

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The QUEX ED and QUEX S device is indicated for use as an Electrophysiological System. The Electrophysiological System is made up of the following Items which are functions of the QUEX ED and S device:

1. Two channel EEG:

The QUEX ED and S Device is intended to record and store EEG signals, and to present the EEG signals in visual formats in real time. The visual signals assist trained medical staff to make neurological diagnoses. The EEG device does not provide any diagnoses conclusion about the subject's condition and does not provide any automated alerts of an adverse clinical event. The QUEX ED AND S Device is intended to be used in a professional healthcare facility environment.
2. GSR [galvanic skin response measuring]

The QUEX ED and S device is intended for use by trained healthcare professionals, to detect and monitor electrical signals produced by the body skin conductance by using electrodes placed on the skin.

QUEX ED or QUEX S devices are contains the following components:

• QUEX ED or QUEX S hardware.
• I QUEX Monitor software
• . USB - USB cable for data communication and power supply.
• EEG electrodes (head electrodes)
• Electrodes for GSR measurements (limbs)
• I Cable harnesses for head and limbs.

The QUEX Monitor software is exclusively used for QUEX ED or QUEX S as part of the system.

The EEG electrodes and the electrodes used for GSR measurements are connected to the QUEX ED or S devices. The device makes the necessary gain on the analog signals and digitalize the signals. The digitalized information is acquired and displayed by the QUEX Monitor software. The QUEX monitor software can run on PCs, notebooks with Windows OS.

Electroencephalography (EEG), measures brain wave activity. The system uses the head harness and electrodes to acquire the signals. The QUEX Devices are measures brain activity on two channels. The EEG wave recording electrodes are connected to the forehead with dry electrodes.

The galvanic skin response (GSR, which falls under the umbrella term of electrodermal activity, or EDA) refers to changes in sweat gland activity.

The GSR recording is done via limb electrodes attached to the wrists and ankles.

The provided FDA 510(k) summary (K232779) for the QUEX ED and QUEX S device describes the regulatory submission for an electrophysiological system, specifically for EEG and GSR measurements. While it details acceptance criteria for various technical aspects like electrical safety and electromagnetic compatibility, and states that “All samples passed the acceptance criteria” for these tests, it does not describe a clinical study meeting the criteria of a multi-reader multi-case (MRMC) comparative effectiveness study or a standalone algorithm-only performance study on a test set with an established ground truth assessed by multiple experts.

The submission focuses on establishing substantial equivalence to a predicate device (Neurosteer Inc. Neurosteer EEG Recorder K221563) primarily through technical comparisons and bench testing. The "performance data" mentioned in the conclusion refers to these bench tests, not a clinical study involving human readers or algorithmic performance against a clinical ground truth.

Therefore, many of the requested details about acceptance criteria and study designs are not present in this document because the device's substantial equivalence was established through technical specifications and bench testing, not through a clinical performance study as typically seen with AI/ML-driven diagnostic devices.

Here's an attempt to answer the questions based only on the provided text, highlighting what is present and what is absent:

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

The document provides acceptance criteria and performance for various bench tests, not for a clinical diagnostic performance study.

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

This document describes bench tests on hardware/software samples, not a clinical study with a test set of patient data. The sample size is referred to as "All samples." No patient data provenance is applicable given the nature of the tests.

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)

Not applicable. No ground truth based on expert consensus for clinical diagnosis was established as this was not a clinical performance study. The tests focused on electrical, EMC, and signal performance according to industry standards.

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

Not applicable. No clinical test set and human readers for adjudication were involved.

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 study was performed or described. The product is an electrophysiological system for recording and displaying signals to assist trained medical staff in making neurological diagnoses, and it "does not provide any diagnoses conclusion about the subject's condition and does not provide any automated alerts of an adverse clinical event." Therefore, there is no AI component assisting human readers in this context.

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

No standalone algorithm performance study was performed or described. The device is a signal acquisition and display system for human interpretation, not an automated diagnostic algorithm.

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

Not applicable. The "ground truth" for the non-clinical tests was the adherence to specified engineering standards and the device's own technical specifications.

8. The sample size for the training set

Not applicable. This document pertains to the regulatory clearance of a medical device based on its functional and safety performance, not the training of an AI/ML algorithm.

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

Not applicable. No training set or ground truth for such a set is mentioned.

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The SignalNED Device is intended to record and display Quantitative EEG (qEEG) (relative band power, e.g., alpha, beta, delta, theta), which is intended to help the user analyze the EEG. The SignalNED does not provide any diagnostic conclusion about the patient's condition. The device is intended to be used on adults by qualified medical and clinical professionals.

The SignalNED is intended to be used in a professional healthcare environment.

The SignalNED Model RE machine uses 10 patient electrodes (4 left, 4 right, 2 midh are used to form the 8 channels. The SignalNED machine requires the use of the SignalNED Sensor Cap, and the system includes the following components:

• Portable EEG machine (Device)
• I Battery & External Battery Charger
• I SignalNED Sensor Cap
• I SignalNED Sensor Cap Cable

The primary function of the SignalNED Model RE is to rapidly record EEG and derive the Quantitative EEG (qEEG) measurement of Relative Band Power for multiple bands (e.g., alpha, beta, theta) at each electrode. These measurements are intended to help the user analyze the underlying EEG. The SignalNED Model RE (client) achieves its intended without relying on wireless comectivity. The SignalNED RE does not provide any diagnostic conclusion about the patient's condition.

This summary describes the acceptance criteria and the study that proves the SignalNED System (Model RE) meets those criteria, based on the provided FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided document does not specify the sample sizes (e.g., number of subjects, number of EEG recordings) used for the non-clinical performance testing. It only states that "All testing passed acceptance criteria and details are contained in the test report." The data provenance (e.g., country of origin, retrospective or prospective) is also not detailed in this summary.

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

The provided document describes non-clinical performance testing (lead-off detection, noise levels, ADC accuracy, QEEG calculation accuracy, electrical performance, biocompatibility). These tests do not typically involve human experts establishing ground truth in the way a clinical study for diagnostic accuracy would. The ground truth for these tests would be established through defined technical specifications, measurement standards, and validated testing protocols. Therefore, information about the number and qualifications of experts for establishing ground truth is not applicable in this context.

4. Adjudication Method for the Test Set

As the performance testing described is non-clinical and based on technical specifications and standards, an adjudication method (like 2+1 or 3+1) used in clinical studies for discrepancies in expert readings is not applicable here. The acceptance criteria for each test inherently define the "ground truth" to which the device's performance is compared.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided document. The SignalNED System is intended to record and display QEEG, which "is intended to help the user analyze the EEG." It explicitly states, "The SignalNED does not provide any diagnostic conclusion about the patient's condition." This indicates that the device is a tool for professional analysis rather than an AI-driven diagnostic aid for human readers. Therefore, an MRMC study comparing human readers with and without AI assistance is not described.

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

The document describes several standalone performance tests for the device's components and calculations (e.g., Lead Off Detection, Signal Acquisition Noise Levels, Software ADC Conversion Accuracy, Quantitative Electroencephalogram (QEEG) accuracy). These tests are conducted on the algorithm and hardware without human interpretation as part of the primary outcome assessment. For instance, the "Software ADC Conversion Accuracy" and "Quantitative Electroencephalogram (QEEG)" accuracy tests evaluate the algorithm's performance in generating calculated EEG measures.

7. The Type of Ground Truth Used

The ground truth used for the reported performance tests is based on:

• Defined Technical Specifications and Engineering Standards: For tests like Lead Off Detection, Signal Acquisition Noise Levels, Software ADC Conversion Accuracy, EC12:2020 Electrical Performance, and Essential Performance Tests (IEC 80601-2-26).
• Validated Calculation Methods: For the Quantitative Electroencephalogram (QEEG) Relative Band Power calculation, the ground truth would be based on established mathematical and signal processing principles for deriving these metrics from raw EEG data.
• International Biocompatibility Standards: For ISO 10993 series tests (Cytotoxicity, Sensitization, Irritation).

8. The Sample Size for the Training Set

The provided document describes performance testing for substantial equivalence, not the development or validation of a machine learning model with distinct training and test sets in the typical sense. While the device calculates QEEG, the details on how the underlying algorithms were developed or "trained" (if machine learning is involved beyond standard signal processing) are not provided. Therefore, a specific sample size for a "training set" is not mentioned.

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

As information about a distinct "training set" for machine learning algorithms is not provided, the method for establishing ground truth for such a set is also not described. The document focuses on performance testing against established engineering, electrical, and biocompatibility standards.

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The REMI Remote EEG Monitoring System is indicated for use in healthcare settings where near real-time and/or remote EEG is warranted and in ambulatory settings where remote EEG is warranted. REMI uses single patient, disposable, wearable sensors intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for a duration up to 30 days.

The REMI System uses the REMI-Mobile software application that runs on qualified portable general purpose computing platforms. REMI-Mobile displays user setup information to trained medical professionals and provides notifications to medical professionals and ambulatory users. REMI-Mobile receives and transmits data from connected REMI Sensors to the secure REMI-Cloud where it is stored and prepared for review on qualified EEG viewing software.

REMI does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. The REMI System is indicated for use with adult and pediatric patients (6+ years).

The REMI System has three major components:

1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
2. REMI Mobile A mobile medical application that is designed to run on a qualified commercial-off-the-shelf mobile computing platform (an Android tablet for use in healthcare settings, and a portable/wearable Android smartwatch for use in ambulatory settings), acquire EEG data transmitted from REMI Sensors and then transmit the EEG data and associated patient information via wireless encrypted transmission to,
3. REMI Cloud A HIPAA-compliant secure cloud storage and data processing platform where data is processed into a qualified EEG reviewing software format for neurological review.

This 510(k) submission includes the addition of the Android smartwatch for ambulatory use and increases the duration of monitoring to up to 30 days.

The provided text describes the REMI Remote EEG Monitoring System and its substantial equivalence to a predicate device. However, it does not include specific quantitative acceptance criteria or detailed study results that would typically be associated with performance metrics like sensitivity, specificity, accuracy, or effect sizes for AI assistance. The document focuses on demonstrating substantial equivalence through testing of electrical safety, wireless technology, software, and human factors.

Here's an attempt to answer your questions based on the available information, with acknowledgements where information is missing.

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

Based on the provided text, the acceptance criteria are generally framed around meeting regulatory standards and functional requirements rather than quantitative performance metrics for diagnostic accuracy.

• Wireless connection maintained for a minimum of 48 continuous hours. |
  | Environmental/Shelf life | Accelerated aging and subsequent functional verification testing conducted. (No specific performance metrics are given, but implies successful completion). |
  | Packaging Performance | Ship testing and subsequent functional verification testing conducted. (No specific performance metrics are given, but implies successful completion). |
  | Biocompatibility | Patient-contacting components verified with Irritation, Sensitization, and Cytotoxicity testing per ISO 10993-5:2009 and ISO 10993-10:2010 for a prolonged time period. (Identical to predicate device). |
  | Usability/ Human Factors | Evaluated tasks associated with use of the device. (Implies successful evaluation, no specific outcomes provided). |
  | Software Functionality | Updated REMI Mobile software successfully supports portable/wearable ambulatory use by initiating sessions from a primary computing platform (Android tablet) to a portable/wearable computing platform (Wear OS smartwatch). |
  | Bench Testing (End-to-End System Performance) | - Able to acquire EEG signals using REMI Sensors and transmit to REMI Mobile software.
• REMI Mobile able to transfer EEG data to REMI Cloud.
• Final EEG file format within REMI Cloud is viewable in qualified EEG viewing software.
• System meets its Essential Performance (record digitized EEG data with patient-applied sensors, transfer wirelessly to cloud-based archive) and fulfills system requirements. |

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

The document does not specify sample sizes for any of the described tests. It mentions "testing conducted," "accelerated aging," "ship testing," and "human factors/usability testing," but provides no details on the number of units, subjects, or data points involved. Similarly, data provenance (country of origin, retrospective/prospective) 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 describe any study establishing ground truth with expert review for a diagnostic purpose. The device is explicitly stated to "not make any diagnostic conclusion" and is "intended as a physiological signal monitor." Therefore, this question is not applicable in the context of the provided information.

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

Since no expert-based ground truth establishment or diagnostic performance evaluation is detailed, there is no mention of an adjudication method.

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 study is mentioned. The device is a physiological signal monitor and does not involve AI assistance for human readers in a diagnostic context.

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

The device itself is a system for acquiring and transmitting EEG data for review by medical professionals on qualified EEG viewing software. It does not perform standalone diagnostic algorithms. Its "Essential Performance" is to record digitized EEG data and transfer it.

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

Given that the device is a physiological signal monitor and "does not make any diagnostic conclusion," the concept of "ground truth" as typically used for diagnostic or screening devices (e.g., pathology, expert consensus on a disease state) is not applicable here. The ground truth for its performance would be the accuracy of EEG signal acquisition and transmission, which is assessed through bench testing and compliance with electrical standards.

8. The sample size for the training set

The document does not describe any machine learning or AI-based component that would require a "training set." The software updates mentioned are for supporting new hardware (smartwatch) and extending monitoring duration, not for developing new diagnostic algorithms.

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

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

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The NEBA® Compact EEG 2R Mobile Headset) is intended to capture, amplify, and wirelessly transmit electrical brain activity for review by a trained medical professional using the CEEG2R recording software. The NEBA Headset comprises an electrode positioning system for placing single-use disposable electrodes on the head (Vermed® A10005, ANSI/AAMI EC12 compliant silver/silver-chloride (Ag/AgCl) electrodes). Contained within the headset is a wireless EEG amplifier module. The NEBA Headset and its associated software do not provide any diagnostic conclusion or automated alerts of any clinical event about a patient's condition. The NEBA Headset transmits electrophysiological signals from the electrodes to a CEEG 2R IEC/UL 60950-1 safety compliant computer running the NEBA CEEG 2R recording software. The system supports six electrode locations (CZ, left ocular, right ocular, left ear, right ear, and ground) on four flat flexible leads and one plastic tab (the latter to support the CZ electrode). The CEEG2 Headset is intended for use in clinical settings in individuals six years of age and older. (Rx only).

The NEBA® Compact EEG2R Mobile Headset is a battery powered (3.7v Lithium-lon battery wirelessly charged using a Qi-compliant receiver) wireless (Bluetooth LE) EEG headset which facilitates the placement of EEG electrodes. The NEBA® Compact EEG2R Mobile Headset (CEG2R) has both hardware and software components. Hardware comprises an EEG electrode system that serves to conduct EEG potentials from the human scalp for transfer to a built-in wireless EEG amplifier. The software provides EEG amplifier hardware control, recording, storage, and user interfaces for waveform monitoring, patient information entry and storage, and accessing stored EEG data. The CEEG2R Headset has two primary purposes: To aid in EEG electrode positioning on the head such that electrodes are positioned in intended locations accurately and reliably per the standard 10-20 International Electrode Placement System. The default leads configuration includes CZ, left ocular (OC-L), right ocular (OC-R), left ear (E-L), right ear (E-R), and Ground (GND) positions; To transmit electrophysiological signals from positioned electrodes to an EEG recording and monitoring device via an internal EEG amplifier with wireless transfer communication. The CEEG2R Headset is constructed using biocompatible patient contact surfaces, the major components formed of polyurethane and polyester. The headset is held in place using a flexible platform at the top of the head and adjustable arms that terminate at lateral supports at the sides of the head. The headset is designed for use with accessory disposable silver/silver-chloride (Ag/AgCl) electrodes (K781430). An integral, counter posing pressure tab accepts a disposable electrode for placement at the top medial portion of the head (at location CZ of the 10-20 International Electrode Placement System). Connection components facilitate the quick insertion and removal of the electrodes and flexible leads. Electrodes for use on the scalp and hair bearing scalp region use a low-viscosity integrated wet-gel conductive medium embedded in the electrodes along with integrated low-tack adhesive optimized for hair compatibility and system stability. The system is stabilized on the head through use of low tack adhesive-lined electrodes, conformable lateral support linings, spring hinges, and a flexible headband platform. The system interfaces with the CEG2R recording and monitoring software for signal acquisition, signal measurement, and electrode impedance measurement by way of a built-in wireless amplifier. The battery, wireless battery charger, wireless communication transmitter and amplifier printed circuit board (PCB) are located atop a midline-located headband platform of the headset.

The NEBA® Compact EEG2R Mobile Headset is intended to capture, amplify, and wirelessly transmit electrical brain activity for review by a trained medical professional. The device itself does not provide diagnostic conclusions or automated alerts. The FDA letter and K223628 Summary provide information about the device's technical specifications and the testing performed to demonstrate its safety and performance relative to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a 510(k) submission, the primary acceptance criteria revolve around demonstating substantial equivalence to predicate devices, focusing on safety and effectiveness. The document lists performance testing against recognized standards rather than specific acceptance metrics like sensitivity/specificity for a diagnostic claim, as it's not a diagnostic device.

Here's a table summarizing the performance standards the device was tested against and, where available, reported values. Explicit acceptance criteria in terms of performance thresholds (e.g., "noise must be less than X" or "dynamic range must be at least Y") are not detailed as specific numerical targets being met, but rather compliance with the standards' requirements.

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

The provided document does not detail any patient-based test sets or data provenance (country of origin, retrospective/prospective) for clinical performance evaluation. The device is cleared through a 510(k) pathway by demonstrating substantial equivalence to predicate devices primarily through engineering and bench testing, as well as software validation and biocompatibility assessments.

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

Given that "clinical testing was not performed to demonstrate substantial equivalence," there was no ground truth established by experts for a patient test set. The performance evaluations were laboratory-based, adhering to recognized standards.

4. Adjudication Method for the Test Set

As no clinical test set requiring human interpretation or adjudication was conducted for performance claims, there was no adjudication method described.

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

No MRMC comparative effectiveness study was done. The device emphasizes capturing and transmitting EEG data for review by a trained medical professional; it does not provide diagnostic conclusions or automated alerts, and therefore, its "effectiveness" in improving human reader performance with AI assistance is not applicable to its current stated intended use.

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

The device itself is an EEG headset with an amplifier and software for recording/review. It does not perform automated diagnostic algorithms. Therefore, no standalone algorithm performance study was conducted. The performance assessed relates to the hardware and software functioning correctly to acquire and transmit EEG signals according to engineering standards.

7. The Type of Ground Truth Used

The "ground truth" for the various performance tests was defined by the specifications and requirements of the relevant industry standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 60601-2-26, ISO 10993-5) and internal design specifications (e.g., input dynamic range, input noise). For biocompatibility, the chemical composition and biological response of materials according to ISO standards served as the ground truth.

8. The Sample Size for the Training Set

There is no mention of a training set because this device does not utilize machine learning or AI algorithms that would require training data for diagnostic or interpretive functions.

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

Since no training set was used, there is no information on how its ground truth would have been established.

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The Neurosteer EEG Recorder is intended to record and store EEG signals, and to present the EEG signals in visual formats in real time. The visuals assist trained medical staff to make neurological diagnoses. The EEG Recorder does not provide any diagnostic conclusion about the subject's condition and does not provide any automated alerts of an adverse clinical event. The EEG Recorder is intended to be used in a professional healthcare facility environment.

The Neurosteer brain monitoring platform is a portable single-channel EEG that measures and records electrical activity of the brain. An adhesive electrode strip is affixed to the subject's forehead to capture the brain activity signal. The strip is attached to a sensing device that transmits the signal via low-energy Bluetooth (BLE) to a local brain activity monitor.

The brain activity monitor provides technical status indicators about the recording (such as battery level, connection status, and electrode disconnection alert). It can also be used to provide auditory prompt sequences (using an external speaker) during monitoring.

The signal is sent via a secure Internet connection to the cloud (either wireless Wi-Fi or physical Ethernet) where the data is stored according to HIPAA quidelines. Data processing performed in the cloud transforms the raw electrical brain activity signal into a display representation of the signal.

When the Internet connection is available, both the raw and processed data can be viewed in real-time on the brain activity monitor and through the web portal. When the connection is not available, only the raw data can be viewed in real-time on the brain activity monitor.

For the Neurosteer EEG Recorder (K221563), the acceptance criteria and the study proving the device meets these criteria are outlined below, primarily focusing on its technical performance rather than a diagnostic accuracy study involving human readers. This device is an EEG recorder, not a diagnostic AI tool, and thus the performance evaluation is centered on its ability to accurately record and present EEG signals.

1. Table of Acceptance Criteria and Reported Device Performance

The Neurosteer EEG Recorder's performance was assessed against established standards for EEG devices and electrodes. The table below summarizes the key acceptance criteria and the corresponding reported performance.

• Recovery to

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The REMI Platform is intended to be used in healthcare settings where near real-time and/or remote EEG is warranted. REMI consists of Epilog disposable Sensors - a single patient, disposable, wearable sensor intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for up to 48 hours. The REM-Mobile software and REMI-Tablet are intended to receive and transmit data from four Epilog Sensors to secure cloud storage for subsequent viewing and reviewing of EEG on third-party software.

REMI does not make any diagnosis or recommendations and is intended only as a physiological signal monitor. Epilog Sensors are intended for use by trained medical professional healthcare facility environment.

Epilog Sensors are intended for use with adult and pediatric patients (6+). (Rx only).

REMI amplifies the electroencephalogram (EEG) from a patient's scalp. After amplification, the EEG are sent to the REMI-Tablet running the REMI-Mobile Application. REMI-Mobile combines the EEG from four Epilog Sensors and patient information and relays the data to a cloud server running Persyst software. The EEG data is accessible through the Persyst Mobile interface. REMI is designed for use with adult and pediatric patients (6+). The user interface for the REMI-Tablet is an 10" LCD touchscreen display.

The user interface for Epilog Sensors is a single button kevpad overmolded in each Sensor, REMI-Tablet power is through A/C adapter as well as limited onboard rechargeable battery. Epilog Sensor power is through a single-use primary coin cell. Using its wireless link, the Epilog Sensors can exchange EEG data and commands with the REMI-Mobile application running on the REMI-Tablet.

REMI has three major components:

1. Epilog-D disposable EEG sensors,

2. REMI-Mobile - mobile OS application designed to run on a medical-grade tablet, acquire EEG data transmitted from Epilog devices along with user-entered patient and device information, and then transmit the EEG data and patient/device information via wireless encrypted WiFi to.

3. REMI-Cloud – A HIPAA-compliant cloud storage and data processing platform where data is processed into a format that a FDA-cleared (K171184) EEG reviewing software called Persyst can use, which will allow remote neurological review.

The firm Epitel, Inc. did not conduct a clinical study to prove the device meets acceptance criteria. Instead, they performed non-clinical performance testing and biocompatibility testing. The device is a physiological signal monitor and does not make diagnoses or recommendations. Therefore, the information provided below is a summary of the non-clinical and biocompatibility tests performed.

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

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The Byteflies Kit is intended for prescription use in the home, healthcare facility, or clinical research environment to acquire, record, and transmit electrical activity of the brain by placing non-invasive electrodes on the head of patients. It acquires, records and transmits two channels of electroencephalogram (EEG) data. The medical use of data acquired by the Byteflies Kit is to be performed under the direction of a licensed medical professional. The Bytellies Kit does not provide any diagnostic conclusions about the patient's condition.

The Byteflies Kit is a wearable medical device for continuous recording of non-invasive physiological signals in healthcare and home settings. The Byteflies Kit is intended to be configured by a trained healthcare professional and consists of 3 main components:

1. Sensor Dot: a biopotential wearable sensor that measures up to 2 bipolar channels of electroencephalography (EEG). It is powered by a rechargeable battery, can record data for up to 24 hours on a single charge, and has an LED indicator to report the operating status to the user. The sample-level EEG data is continuously stored on the Sensor Dot for later retrieval.
2. Sensor Patch: the interface between a Sensor Dot and non-invasive biopotential electrodes (not provided). It attaches magnetically to the Sensor Dot and has four DIN 42802 connectors. Two commercial disposable EEG electrodes per Sensor Patch channel are connected to the head of the subject in a reduced EEG montage. The Sensor Dot attached to the Sensor Patch is carried by the patient to continuously measure electrical brain activity.
3. Docking Station: up to 5 Sensor Dots can connect magnetically to the Docking Station and transfer recorded data to the Docking Station, while charging their batteries. The recorded sample-level EEG signals can then be downloaded from the Docking Station's Management Interface to a computer via a local WiFi network for long-term storage and further review by a healthcare professional. An AC/DC adapter with micro-USB cable supplies power to the Docking Station.

The provided text is a 510(k) summary for the Byteflies Kit, an electroencephalograph device. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed performance data often found in clinical trial reports for AI/CAD devices.

Therefore, much of the requested information regarding acceptance criteria, test set details (sample size, provenance, expert adjudication), MRMC studies, standalone performance, and ground truth for training/testing is not available in the provided document. The document primarily relies on non-clinical (bench) testing and comparison to a predicate device.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available:

Acceptance Criteria and Device Performance

The Byteflies Kit is an electroencephalograph (EEG) device, not an AI/CAD system for diagnostic purposes. Its performance is evaluated against electrical safety and electromagnetic compatibility standards, and its ability to accurately acquire, record, and transmit two channels of EEG data. The document asserts that the device meets these requirements and is substantially equivalent to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) for an EEG device primarily asserting substantial equivalence based on technical specifications and adherence to standards, explicit "acceptance criteria" and "reported device performance" in the context of an AI/CAD diagnostic study (like sensitivity, specificity, AUC) are not present. Instead, the "performance" is demonstrated through compliance with various safety and electrical standards.

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

The document states, "The Byteflies Kit is an electroencephalographic device comprised of hardware and software that has been bench tested to assess safety and effectiveness, and to establish substantial equivalence with the predicate device. We believe further clinical data is not required to demonstrate performance of the Byteflies Kit for the indications for use subject to this submission."

• Sample Size for Test Set: Not applicable/not provided. The "testing" referred to is primarily bench testing for electrical, EMC, and biocompatibility standards, not a clinical test set of patient data.
• Data Provenance (Country of origin, retrospective/prospective): Not applicable, as no clinical test set data is described. The tests were performed by "Independent UL-certified laboratory testing."

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

• Number of Experts: Not applicable/not provided. No clinical ground truth establishment process is described as clinical data was deemed "not required."
• Qualifications of Experts: Not applicable/not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/not provided. No clinical test set requiring adjudication is 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

• MRMC Study: No. This device is an EEG acquisition and recording system, not an AI or CAD (Computer-Aided Detection/Diagnosis) device that assists human readers with interpretation.

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

• Standalone Performance: Not applicable. This device is not an algorithm for diagnostic interpretation. Its "performance" is in signal acquisition and adherence to technical standards.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Not applicable. The device's function is to acquire EEG signals. The accuracy and quality of these signals are determined by compliance with the aforementioned technical standards (e.g., signal fidelity under IEC 60601-2-26), rather than by comparison to a "ground truth" of a disease state. The document explicitly states the device "does not provide any diagnostic conclusions about the patient's condition," meaning it's not classifying or diagnosing.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This is a hardware device for signal acquisition, not a machine learning model that requires a training set.

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

• Ground Truth for Training Set: Not applicable. As it is not an AI/ML device, there is no training set or associated ground truth.

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