API¶
Compute phase-amplitude coupling¶
tensorpac
:
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Compute Phase-Amplitude Coupling (PAC). |
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Compute the Event Related Phase-Amplitude Coupling (ERPAC). |
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Compute the Preferred Phase (PP). |
Utility functions¶
tensorpac.utils
:
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Power Spectrum Density for electrophysiological brain data. |
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Compute the Inter-Trials Coherence (ITC). |
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Bin the amplitude according to the phase. |
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Peak-Locked Time-frequency representation. |
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Generate cross-frequency coupling vectors. |
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Generate triangular vector. |
Generate synthetic signals¶
tensorpac.signals
:
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Generate artificially phase-amplitude coupled signals using wavelets. |
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Generate artificially phase-amplitude coupled signals. |
Statistics¶
tensorpac.stats
:
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Test the stationarity of an electrophysiological dataset. |
Individual methods¶
tensorpac.methods
:
PAC methods¶
If you don’t want to use the tensorpac.Pac
class, you can also manually import the method of your choice
and use it on phase / amplitude to compute PAC. Note that some functions have both a tensor or Numba-based implementation.
Tensor-based implementation¶
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Tensor-based Mean Vector Length (MVL). |
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Tensor-based Modulation index (MI). |
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Tensor-based Heights ratio (HR). |
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Tensor-based Normalized direct Pac (ndPAC). |
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Tensor-based Phase Locking-Value (PLV). |
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Tensor-based Gaussian Copula PAC (gcPac). |
Numba-based implementation¶
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Numba-based Mean Vector Length (MVL). |
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Numba-based Modulation index (MI). |
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Numba-based Heights ratio (HR). |
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Numba-based Normalized direct Pac (ndPAC). |
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Numba-based Phase Locking-Value (PLV). |
Preferred phase¶
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Compute the preferred phase. |
Surrogates methods¶
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Compute surrogates by swapping phase / amplitude trials. |
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Compute surrogates by swapping amplitudes time blocks. |
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Compute surrogates by introducing a time lag on phase series. |
Normalization¶
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Normalize the phase amplitude coupling. |