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Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure.

Jerrin Thomas Panachakel, Ramakrishnan A G

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    Summary

    Imagining different speech sounds, like nasal versus bilabial consonants, creates distinct brainwave patterns. This finding improves brain-computer interfaces (BCIs) by enabling better selection of speech prompts for communication aids.

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    Area of Science:

    • Neuroscience
    • Linguistics
    • Biomedical Engineering

    Background:

    • Phonological categories are crucial for speech articulation.
    • Brain-computer interfaces (BCIs) require distinct neural signals for effective use.
    • Speech imagery-based BCIs face challenges in identifying suitable prompts.

    Purpose of the Study:

    • To investigate if phonological categories of imagined speech prompts elicit different cortical phase synchronization patterns.
    • To determine if electroencephalography (EEG) data from imagined speech can be classified based on phonological categories.
    • To assess the efficacy of neural network (NN) classifiers using Mean Phase Coherence (MPC) versus statistical parameters.

    Main Methods:

    • EEG data was collected during imagined speech of nasal and bilabial consonants.
    • Mean Phase Coherence (MPC) was computed to measure phase synchronization across cortical regions.
    • Shallow neural networks (NNs) were trained to classify EEG data based on MPC values and statistical parameters.

    Main Results:

    • The NN classifier trained on beta-band MPC values achieved superior classification accuracy compared to alpha-band, gamma-band MPC, or statistical parameters.
    • Distinct phase synchronization patterns were observed between imagined nasal and bilabial consonants.
    • The study demonstrates the discriminative potential of phonological features in EEG during speech imagery.

    Conclusions:

    • Phonological categories, specifically nasal versus bilabial consonants, induce discernible differences in cortical phase synchronization during speech imagery.
    • Beta-band phase synchronization is a robust feature for classifying imagined speech prompts.
    • This research provides a foundation for selecting more effective speech prompts in BCI applications, enhancing assistive technologies for individuals with disabilities.