Brain Engineering

Portable hybrid fNIRS-EEG Device for Early Diagnosing Dementia

  • A new technology for diagnosing dementia
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    Existing Dementia Diagnosis Technology

    Conventional Dementia Diagnosis Procedure

    1. Medical History Checkup
    2. Direct Examination
    3. Causative Diseases Test

    Existing Causative Diseases Diagnosis Techniques

    • Brain MRI (Expensive)
    • Cerebrospinal Fluid Drawn(Invaisive)
    • Neuropsychological Examination(Non-quantitative)
    • Medical Laborotory Test (Invaisive)
    • ApoE Genotype (Invaisive)
    • Magnetic Resonance Imaging (MRI) Features: 3D Imaging, High-resolution
      Disadvantages: Strong Magnetic Field, Non-portability, Expensive
    • Computed Tomography (CT) Features: Sectional Imaging, Short Measuring Time
      Disadvantages: X-ray Transmission, Non-portability, Expensive
    • Positron Emission Tomography (PET) Features: Glucose Uptake Measuring
      Disadvantages: Radiopharmaccutical Compounds Injection, Non-portability, Expensive

    Need for New Dementia Diagnosis Technology

    Brain Imaging Examination using MRI, CT, and PET
    • Examination in a daily life is not possible
    • Too expensive
    • Non-realtime Monitoring
    • Expert diagnosis is essential
    • Possiblility of Harm to Human Body
    Necessity of simple, real-time, portable, and low-cost home care device in everyday life
    1. Online and Real-time Monitoring
    2. Non-invaisive Diagnosis
    3. Quatitaive Method for Diagnosing Demntia
    4. Low-cost Device
    5. Health Care Device for early Diagnosing Dementia
  • Proposed technical convergence using fNIRS-EEG
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    Overview of proposed Hybrid fNIRS-EEG

    • Real-time cerebral hemodynamic response measurement based on fNIRS
    • Real-time measurement of continuous rhythmic oscillation of brain waves based on EEG
    • Appratus for portable using employing wearable hybrid fNIRS-EEG and Mobile Application

    fNIRS (Cerebral Hemodynami Response Measurement System) EEG (Brain Wave Measurement System)
    Functional near-infrared spectroscopy Electroencephalography
    • Near-infrared (600 nm ~ 1000 nm) wavelenth used
    • Hemodynamic response measurement (HbO, HbR Concentration Rate)
    • Non-invaisive optical method
    • Portable, real-time, low-cost, wireles, harmless
    • Spatial resolution better than EEG
    • Potential changes (EEG) measurement occurring in the cerebral nerves
    • Cerebral activation examination by analyzing frequency of potential changes (EEG)
    • Non-invaisive method
    • Portable, low-cost, wireles, harmless
    • Time resolution better than fNIRS
  • Hybrid fNIRS-EEG Development
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    Hybrid fNIRS-EEG H/W Development

    Hybrid fNIRS-EEG H/W Development
    Absortion rate of HbO, HbR, and water according to wavelentgh and real-time hemodynamic response measusrement system
    Development Target
    • Hybrid fNIRS-EEG Device Development
    • Effective channels: Over 52 Channels fNIRS / Over 6 Channels EEG
    • Sampling frequency: Over 5 Hz fNIRS / Over 200 Hz EEG
    • Database construction: Data amplification using Deep Learning Techcnique (DCGAN, LSGAN)
    • Signal processing and analysis: Mobile embedded Java, Python Program
    • Performance verfication through comparison between development device and conventional device
    • System design and development for real-time hemodynamic response analysis
    • Real-time 3D activation map and monitoring system based on fNIRS-EEG
  • Diagnosing Database using DCGAN and Activation Map
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    Database building algorithm design using Deep Learning

    • Activation map creation based on fNIRS-EEG data of the prefrontal cortex for diagnosis of patients with demetia or mild cognitive impairment
    • Improvement of diagnosis accuracy by building and analyzing database using DCGAN and/or LSGAN
    • Offline database construction and diagnostic accuracy evaluation by measuring fNIRS-EEG signal while performing work memory test such as SVFT, N-back, Stroop task, etc.
    Improvement of diagnosis accuracy by building and analyzing database using DCGAN and/or LSGAN
  • Digital Health Care System for Dementia Self-diagnosis and Monitoring
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    Development of healthcare system for self-diagnosis and monitoring of dementia or mild cognitive impairment

    • Measurement and real-time monitoring for the general public using the developed hybrid fNIRS-EEG device
    • Real-time 3D brain imaging application S/W development
    • Big data platform for real-time diagnosis using data amplified through DCGAN/LSGAN
    • Introduction of healthcare system and service after verification of safety, accuracy, efficay, and certification/li>

    Features of the proposed technology

    Portabl hybrid fNIRS-EEG Device for early diagnosing dementia

    Development Target

    Real-time brain signal measurement device and application S/W -> Rapid and accurate dementia diagnosis system

    Features
    • Brain health care system using portable hybrid fNIRS-EEG
    • Application S/W for real-time monitoring/diagnosis using big data and AI
    • Commercialization of dementia diagnosis system for digital healthcare

    Digital home care solution for brain health management