Beamformers

class invert.solvers.beamformer.SolverESMV(name='ESMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Eigenspace-based Minimum Variance (ESMV) Beamformer

inverse solution [1].

forward

The mne-python Forward model instance.

Type

mne.Forward

References

[1] Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

make_inverse_operator(forward, mne_obj, *args, alpha='auto', **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverHOCMCMV(name='HOCMCMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Higher-Order Covariance Multiple Constrained Minimum Variance (HOCMCMV)

Beamformer inverse solution [1].

forward

The mne-python Forward model instance.

Type

mne.Forward

References

[1] Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', order=3, verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

• order (int) – The order of the covariance matrix. Should be a positive integer not evenly divisible by two {3, 5, 7, …}

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverHOCMV(name='HOCMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Higher-Order Covariance Minimum Variance (HOCMV)

Beamformer inverse solution [1].

forward

The mne-python Forward model instance.

Type

mne.Forward

References

[1] Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', order=3, verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

• order (int) – The order of the covariance matrix. Should be a positive integer not evenly divisible by two {3, 5, 7, …}

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverLCMV(name='LCMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Linearly Constrained Minimum Variance Beamformer (LCMV)

inverse solution [1].

References

[1] Van Veen, B. D., & Buckley, K. M. (1988). Beamforming: A versatile approach to spatial filtering. IEEE assp magazine, 5(2), 4-24.

make_inverse_operator(forward, mne_obj, *args, alpha='auto', weight_norm=True, verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverMCMV(name='MCMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Multiple Constrained Minimum Variance (MCMV) Beamformer inverse solution [1].

References

[1] Nunes, A. S., Moiseev, A., Kozhemiako, N., Cheung, T., Ribary, U., & Doesburg, S. M. (2020). Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses. NeuroImage, 208, 116386.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, noise_cov=None, alpha='auto', verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverMVAB(name='Minimum Variance Adaptive Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Minimum Variance Adaptive Beamformer (MVAB) inverse

solution [1].

References

[1] Vorobyov, S. A. (2013). Principles of minimum variance robust adaptive beamforming design. Signal Processing, 93(12), 3264-3277.

make_inverse_operator(forward, mne_obj, *args, alpha='auto', **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverReciPSIICOS(name='ReciPSIICOS', reduce_rank=True, rank='auto', **kwargs)

Class for the Reciprocal Phase Shift Invariant Imaging of Coherent Sources (ReciPSIICOS) Beamformer inverse solution [1].

References

[1] Kuznetsova, A., Nurislamova, Y., & Ossadtchi, A. (2021). Modified covariance beamformer for solving MEG inverse problem in the environment with correlated sources. Neuroimage, 228, 117677.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverSAM(name='SAM Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Synthetic Aperture Magnetometry Beamformer (SAM) inverse solution [1].

References

[1] Robinson, S. E. V. J. (1999). Functional neuroimaging by synthetic aperture magnetometry (SAM). Recent advances in biomagnetism.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', verbose=0, **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverSMV(name='SMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Standardized Minimum Variance (SMV) Beamformer inverse

solution [1].

References

[1] Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

class invert.solvers.beamformer.SolverWNMV(name='WNMV Beamformer', reduce_rank=True, rank='auto', **kwargs)

Class for the Weight-normalized Minimum Variance (WNMV) Beamformer

inverse solution [1].

forward

The mne-python Forward model instance.

Type

mne.Forward

References

[1] Jonmohamadi, Y., Poudel, G., Innes, C., Weiss, D., Krueger, R., & Jones, R. (2014). Comparison of beamformers for EEG source signal reconstruction. Biomedical Signal Processing and Control, 14, 175-188.

make_inverse_operator(forward, mne_obj, *args, weight_norm=True, alpha='auto', **kwargs)

Calculate inverse operator.

Parameters

• forward (mne.Forward) – The mne-python Forward model instance.

• mne_obj ([mne.Evoked, mne.Epochs, mne.io.Raw]) – The MNE data object.

• weight_norm (bool) – Normalize the filter weight matrix W to unit length of the columns.

• alpha (float) – The regularization parameter.

Returns

self

Return type

object returns itself for convenience

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