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Meditation EEG Results

Summary

  • There were issues with data quality due to different montages and preprocessing steps increasing variability.
  • The machine learning classification was very accurate 97% at discriminating pre-meditation and endmeditation, indicating a difference between EEG co-spectra for these conditions.
  • There was a relationship between time in meditation and probability of end-meditation classification, with D2S2 faster at inducing end-meditation state than D3S2.
  • There were differences in the EEG power bands, with each meditation technique inducing different patterns of changes in the power bands.

Summary

  • There were differences in the EEG power bands, with each meditation technique inducing different patterns of changes in the power bands.
    • No filtering
    • 0.1-60 Hz band-pass
    • 0.5-80 Hz band-passThere were 3 different types of filtering previously applied
  • The machine learning classification was very accurate 97% at discriminating pre-meditation and endmeditation, indicating a difference between EEG co-spectra for these conditions.
  • There was a relationship between time in meditation and probability of end-meditation classification, with D2S2 faster at inducing end-meditation state than D3S2.
  • There were differences in the EEG power bands, with each meditation technique inducing different patterns of changes in the power bands.
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