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Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs.

Foroogh Shamsi, Ali Haddad, Laleh Naja Zadeh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    Electroencephalography (EEG) can distinguish motor imagery tasks performed with or without pain. The gamma frequency band in EEG data is crucial for differentiating these pain states during imagined movements.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Cognitive Science

    Background:

    • Distinguishing between pain and pain-free states is crucial for patient care and rehabilitation.
    • Motor imagery, the mental simulation of movement, offers a non-invasive window into brain activity.
    • Electroencephalography (EEG) is a valuable tool for capturing brain signals during cognitive tasks.

    Purpose of the Study:

    • To investigate if electroencephalography (EEG) can differentiate motor imagery tasks performed under pain versus pain-free conditions.
    • To identify which frequency bands are most effective in discriminating between these states.
    • To explore the potential of brain-computer interfaces for pain management.

    Main Methods:

    • Utilized electroencephalography (EEG) recordings from participants performing four motor imagery tasks (right hand, left hand, foot, tongue).
    • Employed a functional connectivity-based feature extraction method.
    • Classified pain-free versus under-pain conditions using a long short-term memory (LSTM) classifier.
    • Analyzed classification performance across different EEG frequency bands.

    Main Results:

    • Achieved an average classification accuracy of 77.86-80.04% when considering all frequency bands.
    • The gamma frequency band demonstrated significantly higher accuracy in discriminating pain/no-pain conditions compared to other bands.
    • Delta and theta bands provided limited discriminatory information between pain and pain-free states.

    Conclusions:

    • This study is the first to demonstrate the discrimination of motor imagery tasks based on pain status using EEG.
    • The gamma band is critical for differentiating pain and no-pain states during motor imagery.
    • Findings suggest potential for developing brain-computer interface-based assistive technologies for pain management.