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    Summary
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    This study introduces a hardware-efficient processor for classifying human emotions in patients with chronic neurological disorders (CNDs) using scalp EEG. The system achieves over 72% accuracy, aiding in early intervention for conditions like Alzheimer's and ASD.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Chronic neurological disorders (CNDs) like Alzheimer's, ASD, and ALS degrade cognitive and emotional abilities.
    • Continuous neuro-feedback monitoring is vital for managing CNDs' severe effects.
    • Early intervention through preemptive measures can alleviate CNDs' impact.

    Purpose of the Study:

    • To present a hardware-efficient, dedicated processor for human emotion classification in CND patients.
    • To enable continuous, non-invasive monitoring for better CND management.
    • To improve early detection and intervention strategies for CNDs.

    Main Methods:

    • Utilized scalp electroencephalography (EEG) for emotion classification based on valence and arousal.
    • Employed a linear support vector machine (SVM) classifier with power spectral density features.
    • Developed a novel look-up-table based logarithmic division unit (LDU) for efficient feature extraction in machine learning (ML).

    Main Results:

    • Achieved classification accuracies of 72.96% for valence and 73.14% for arousal.
    • The implemented LDU reduced integer division cost by 34% for ML applications.
    • The 2x3mm² processor fabricated using a 0.18μm CMOS process demonstrated low power (2.04 mW) and energy (16 μJ/classification) consumption.

    Conclusions:

    • The developed processor offers a viable, hardware-efficient solution for non-invasive emotion classification in CND patients.
    • This technology can support continuous monitoring and facilitate early intervention strategies.
    • The efficient LDU design contributes to cost reduction in ML applications for wearable systems.