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.