MNELIB::MNEMneData Class Reference

Data associated with MNE computations for each mneMeasDataSet. More...

#include <mne_mne_data.h>

Public Types
typedef QSharedPointer< MNEMneData >	SPtr
typedef QSharedPointer< const MNEMneData >	ConstSPtr

Public Member Functions
	MNEMneData ()=default
	~MNEMneData ()=default
void	computeRegularization (const MNEInverseOperator &inv, const Eigen::MatrixXd &data)
void	selectRegularization (const MNEInverseOperator &inv, double snr)
void	computePredicted (const MNEInverseOperator &inv)

Static Public Member Functions
static MNEMneData	compute (const MNEInverseOperator &inv, const Eigen::MatrixXd &data, double snr)

Public Attributes
Eigen::MatrixXd	datap
Eigen::MatrixXd	predicted
Eigen::VectorXd	SNR
Eigen::VectorXd	lambda2_est
Eigen::VectorXd	lambda2

Detailed Description

Data associated with MNE computations for each mneMeasDataSet.

Implements MNE Mne Data (Replaces *mneMneData,mneMneDataRec; struct of MNE-C mne_types.h).

Definition at line 57 of file mne_mne_data.h.

Member Typedef Documentation

ConstSPtr

typedef QSharedPointer<const MNEMneData> MNELIB::MNEMneData::ConstSPtr

Const shared pointer type for MNEMneData.

Definition at line 61 of file mne_mne_data.h.

SPtr

typedef QSharedPointer<MNEMneData> MNELIB::MNEMneData::SPtr

Shared pointer type for MNEMneData.

Definition at line 60 of file mne_mne_data.h.

Constructor & Destructor Documentation

MNEMneData()

MNELIB::MNEMneData::MNEMneData ( )

default

Constructs the MNEMneData.

~MNEMneData()

MNELIB::MNEMneData::~MNEMneData ( )

default

Destroys the MNEMneData.

Member Function Documentation

compute()

MNEMneData MNEMneData::compute ( const MNEInverseOperator & inv, const Eigen::MatrixXd & data, double snr )

static

Fills all fields for one data set: projects it on the eigenfields, estimates the SNR and lambda2 per time point, selects the lambda2 to use and computes the predicted data.

Parameters

[in]	inv	An inverse operator prepared with MNEInverseOperator::prepare_inverse_operator.
[in]	data	Measured data, one row per channel of inv in its order, one column per time.
[in]	snr	Power SNR for a fixed lambda2 (see selectRegularization), <= 0 to use lambda2_est.

Returns

The MNE data.

Definition at line 40 of file mne_mne_data.cpp.

computePredicted()

void MNEMneData::computePredicted

(

const MNEInverseOperator &

inv

)

Computes the data the minimum-norm estimate predicts with the selected lambda2, colored back to the sensors (MNE-C compute_predicted_data; the data part of mne.minimum_norm.apply_inverse's residual).

Parameters

[in]

inv

The prepared inverse operator used for computeRegularization.

Definition at line 95 of file mne_mne_data.cpp.

computeRegularization()

void MNEMneData::computeRegularization	(	const MNEInverseOperator &	inv,
		const Eigen::MatrixXd &	data )

Projects the whitened data on the eigenfields (MNE-C mne_project_to_eigen_fields) and estimates SNR and lambda2_est per time point (MNE-C compute_regularization, noise method).

SNR is the power of the whitened data per non-zero noise eigenvalue (the square of mne.minimum_norm.estimate_snr's snr). lambda2_est is 100 where SNR < 1; elsewhere it starts at 10 and shrinks by 0.9 until the squared prediction error of the whitened data falls below the chi^2 point at p = 0.001 with one degree of freedom less than the components used.

Parameters

[in]	inv	A prepared inverse operator.
[in]	data	Measured data, one row per channel of inv, one column per time.

Definition at line 51 of file mne_mne_data.cpp.

selectRegularization()

void MNEMneData::selectRegularization	(	const MNEInverseOperator &	inv,
		double	snr )

Selects lambda2 per time point (MNE-C select_regularization): trace_ratio / snr for a positive power SNR, lambda2_est otherwise. trace_ratio is the mean squared singular value.

Parameters

[in]	inv	The prepared inverse operator used for computeRegularization.
[in]	snr	Power SNR, <= 0 to use lambda2_est.

Definition at line 84 of file mne_mne_data.cpp.

Member Data Documentation

datap

Eigen::MatrixXd MNELIB::MNEMneData::datap

Projection of the whitened data onto the eigenfields, components x times.

Definition at line 124 of file mne_mne_data.h.

lambda2

Eigen::VectorXd MNELIB::MNEMneData::lambda2

Regularization parameter to be used (as a function of time).

Definition at line 128 of file mne_mne_data.h.

lambda2_est

Eigen::VectorXd MNELIB::MNEMneData::lambda2_est

Regularization parameter estimated from available data.

Definition at line 127 of file mne_mne_data.h.

predicted

Eigen::MatrixXd MNELIB::MNEMneData::predicted

The predicted data, channels x times.

Definition at line 125 of file mne_mne_data.h.

SNR

Eigen::VectorXd MNELIB::MNEMneData::SNR

Estimated power SNR as a function of time.

Definition at line 126 of file mne_mne_data.h.

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The documentation for this class was generated from the following files:

• src/libraries/mne/mne_mne_data.h
• src/libraries/mne/mne_mne_data.cpp