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Classification in emotional BCI using phase information from the EEG.

L Santamaria, C James

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    This study introduces Phase Locking Value (PLV) from electroencephalogram (EEG) for Brain Computer Interface (BCI) classification. Synchronization patterns using PLV show promise for motor imagery (MI) tasks, similar to existing methods.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Synchronization and distributed functional networks are established in engineering.
    • Brain Computer Interfaces (BCI) commonly utilize motor imagery (MI) tasks.
    • Evoked Potentials (EPs) like ERS/ERD are standard BCI classification features.

    Purpose of the Study:

    • To evaluate Phase Locking Value (PLV) from electroencephalogram (EEG) as a novel BCI classification method.
    • To explore synchronization patterns in EEG during a motor imagery (MI) task using emotional stimuli.
    • To identify principal EEG channel pairs exhibiting distinct PLV patterns for task classification.

    Main Methods:

    • Utilized electroencephalogram (EEG) data from participants performing a motor imagery (MI) task.

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  • Employed emotional schematic faces as stimuli during the MI task.
  • Calculated Phase Locking Value (PLV) between EEG channel pairs to quantify synchronization.
  • Identified significant channel pairs based on variations in PLV values across tasks and participants.

    • Main Results:

    • Identified specific EEG channel pairs with significant PLV variations correlating to the MI task.
    • Observed that PLV patterns in selected channel pairs resemble established ERS/ERD patterns.
    • Demonstrated the potential of PLV as a classification feature for MI-based BCIs.

    Conclusions:

    • Phase Locking Value (PLV) is a viable method for classifying motor imagery (MI) tasks in Brain Computer Interfaces (BCI).
    • EEG synchronization patterns, quantified by PLV, offer an alternative to traditional ERS/ERD features.
    • This approach holds potential for advancing BCI technology through improved signal processing.