# -*- coding: utf-8 -*- # Authors: Romain Trachel # Alexandre Gramfort # Alexandre Barachant # Clemens Brunner # Jean-Remi King # # License: BSD-3-Clause import copy as cp import numpy as np from .base import BaseEstimator from .mixin import TransformerMixin from ..cov import _regularized_covariance from ..defaults import _INTERPOLATION_DEFAULT from ..fixes import pinv from ..utils import fill_doc, _check_option, _validate_type, copy_doc @fill_doc class CSP(TransformerMixin, BaseEstimator): """M/EEG signal decomposition using the Common Spatial Patterns (CSP). This class can be used as a supervised decomposition to estimate spatial filters for feature extraction. CSP in the context of EEG was first described in :footcite:`KolesEtAl1990`; a comprehensive tutorial on CSP can be found in :footcite:`BlankertzEtAl2008`. Multi-class solving is implemented from :footcite:`Grosse-WentrupBuss2008`. Parameters ---------- n_components : int (default 4) The number of components to decompose M/EEG signals. This number should be set by cross-validation. reg : float | str | None (default None) If not None (same as ``'empirical'``, default), allow regularization for covariance estimation. If float (between 0 and 1), shrinkage is used. For str values, ``reg`` will be passed as ``method`` to :func:`mne.compute_covariance`. log : None | bool (default None) If ``transform_into`` equals ``'average_power'`` and ``log`` is None or True, then apply a log transform to standardize features, else features are z-scored. If ``transform_into`` is ``'csp_space'``, ``log`` must be None. cov_est : 'concat' | 'epoch' (default 'concat') If ``'concat'``, covariance matrices are estimated on concatenated epochs for each class. If ``'epoch'``, covariance matrices are estimated on each epoch separately and then averaged over each class. transform_into : 'average_power' | 'csp_space' (default 'average_power') If 'average_power' then ``self.transform`` will return the average power of each spatial filter. If ``'csp_space'``, ``self.transform`` will return the data in CSP space. norm_trace : bool (default False) Normalize class covariance by its trace. Trace normalization is a step of the original CSP algorithm :footcite:`KolesEtAl1990` to eliminate magnitude variations in the EEG between individuals. It is not applied in more recent work :footcite:`BlankertzEtAl2008`, :footcite:`Grosse-WentrupBuss2008` and can have a negative impact on pattern order. cov_method_params : dict | None Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 %(rank_none)s .. versionadded:: 0.17 component_order : 'mutual_info' | 'alternate' (default 'mutual_info') If ``'mutual_info'`` order components by decreasing mutual information (in the two-class case this uses a simplification which orders components by decreasing absolute deviation of the eigenvalues from 0.5 :footcite:`BarachantEtAl2010`). For the two-class case, ``'alternate'`` orders components by starting with the largest eigenvalue, followed by the smallest, the second-to-largest, the second-to-smallest, and so on :footcite:`BlankertzEtAl2008`. .. versionadded:: 0.21 Attributes ---------- filters_ : ndarray, shape (n_channels, n_channels) If fit, the CSP components used to decompose the data, else None. patterns_ : ndarray, shape (n_channels, n_channels) If fit, the CSP patterns used to restore M/EEG signals, else None. mean_ : ndarray, shape (n_components,) If fit, the mean squared power for each component. std_ : ndarray, shape (n_components,) If fit, the std squared power for each component. See Also -------- mne.preprocessing.Xdawn, SPoC References ---------- .. footbibliography:: """ def __init__(self, n_components=4, reg=None, log=None, cov_est='concat', transform_into='average_power', norm_trace=False, cov_method_params=None, rank=None, component_order='mutual_info'): # Init default CSP if not isinstance(n_components, int): raise ValueError('n_components must be an integer.') self.n_components = n_components self.rank = rank self.reg = reg # Init default cov_est if not (cov_est == "concat" or cov_est == "epoch"): raise ValueError("unknown covariance estimation method") self.cov_est = cov_est # Init default transform_into self.transform_into = _check_option('transform_into', transform_into, ['average_power', 'csp_space']) # Init default log if transform_into == 'average_power': if log is not None and not isinstance(log, bool): raise ValueError('log must be a boolean if transform_into == ' '"average_power".') else: if log is not None: raise ValueError('log must be a None if transform_into == ' '"csp_space".') self.log = log _validate_type(norm_trace, bool, 'norm_trace') self.norm_trace = norm_trace self.cov_method_params = cov_method_params self.component_order = _check_option('component_order', component_order, ('mutual_info', 'alternate')) def _check_Xy(self, X, y=None): """Check input data.""" if not isinstance(X, np.ndarray): raise ValueError("X should be of type ndarray (got %s)." % type(X)) if y is not None: if len(X) != len(y) or len(y) < 1: raise ValueError('X and y must have the same length.') if X.ndim < 3: raise ValueError('X must have at least 3 dimensions.') def fit(self, X, y): """Estimate the CSP decomposition on epochs. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_times) The data on which to estimate the CSP. y : array, shape (n_epochs,) The class for each epoch. Returns ------- self : instance of CSP Returns the modified instance. """ self._check_Xy(X, y) self._classes = np.unique(y) n_classes = len(self._classes) if n_classes < 2: raise ValueError("n_classes must be >= 2.") if n_classes > 2 and self.component_order == 'alternate': raise ValueError("component_order='alternate' requires two " "classes, but data contains {} classes; use " "component_order='mutual_info' " "instead.".format(n_classes)) covs, sample_weights = self._compute_covariance_matrices(X, y) eigen_vectors, eigen_values = self._decompose_covs(covs, sample_weights) ix = self._order_components(covs, sample_weights, eigen_vectors, eigen_values, self.component_order) eigen_vectors = eigen_vectors[:, ix] self.filters_ = eigen_vectors.T self.patterns_ = pinv(eigen_vectors) pick_filters = self.filters_[:self.n_components] X = np.asarray([np.dot(pick_filters, epoch) for epoch in X]) # compute features (mean power) X = (X ** 2).mean(axis=2) # To standardize features self.mean_ = X.mean(axis=0) self.std_ = X.std(axis=0) return self def transform(self, X): """Estimate epochs sources given the CSP filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data. Returns ------- X : ndarray If self.transform_into == 'average_power' then returns the power of CSP features averaged over time and shape (n_epochs, n_sources) If self.transform_into == 'csp_space' then returns the data in CSP space and shape is (n_epochs, n_sources, n_times). """ if not isinstance(X, np.ndarray): raise ValueError("X should be of type ndarray (got %s)." % type(X)) if self.filters_ is None: raise RuntimeError('No filters available. Please first fit CSP ' 'decomposition.') pick_filters = self.filters_[:self.n_components] X = np.asarray([np.dot(pick_filters, epoch) for epoch in X]) # compute features (mean band power) if self.transform_into == 'average_power': X = (X ** 2).mean(axis=2) log = True if self.log is None else self.log if log: X = np.log(X) else: X -= self.mean_ X /= self.std_ return X @copy_doc(TransformerMixin.fit_transform) def fit_transform(self, X, y, **fit_params): # noqa: D102 return super().fit_transform(X, y=y, **fit_params) @fill_doc def plot_patterns(self, info, components=None, ch_type=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scalings=None, units='a.u.', res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp=_INTERPOLATION_DEFAULT, average=None, sphere=None): """Plot topographic patterns of components. The patterns explain how the measured data was generated from the neural sources (a.k.a. the forward model). Parameters ---------- %(info_not_none)s Used for fitting. If not available, consider using :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None. vmin : float | callable The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). vmax : float | callable The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, default np.min(data). If callable, the output equals vmax(data). cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. If 'interactive', translates to (None, True). Defaults to 'RdBu_r'. .. warning:: Interactive mode works smoothly only for a small amount of topomaps. sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be used (via .add_artist). Defaults to True. colorbar : bool Plot a colorbar. scalings : dict | float | None The scalings of the channel types to be applied for plotting. If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. units : dict | str | None The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined. res : int The resolution of the topomap image (n pixels along each side). size : float Side length per topomap in inches. cbar_fmt : str String format for colorbar values. name_format : str String format for topomap values. Defaults to "CSP%%01d". show : bool Show figure if True. show_names : bool | callable If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix 'MEG ' from all channel names, pass the function lambda x: x.replace('MEG ', ''). If ``mask`` is not None, only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. %(mask_patterns_topomap)s %(mask_params_topomap)s %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging. %(sphere_topomap_auto)s Returns ------- fig : instance of matplotlib.figure.Figure The figure. """ from .. import EvokedArray if components is None: components = np.arange(self.n_components) # set sampling frequency to have 1 component per time point info = cp.deepcopy(info) with info._unlock(): info['sfreq'] = 1. # create an evoked patterns = EvokedArray(self.patterns_.T, info, tmin=0) # the call plot_topomap return patterns.plot_topomap( times=components, ch_type=ch_type, vmin=vmin, vmax=vmax, cmap=cmap, colorbar=colorbar, res=res, cbar_fmt=cbar_fmt, sensors=sensors, scalings=scalings, units=units, time_unit='s', time_format=name_format, size=size, show_names=show_names, title=title, mask_params=mask_params, mask=mask, outlines=outlines, contours=contours, image_interp=image_interp, show=show, average=average, sphere=sphere) @fill_doc def plot_filters(self, info, components=None, ch_type=None, vmin=None, vmax=None, cmap='RdBu_r', sensors=True, colorbar=True, scalings=None, units='a.u.', res=64, size=1, cbar_fmt='%3.1f', name_format='CSP%01d', show=True, show_names=False, title=None, mask=None, mask_params=None, outlines='head', contours=6, image_interp=_INTERPOLATION_DEFAULT, average=None): """Plot topographic filters of components. The filters are used to extract discriminant neural sources from the measured data (a.k.a. the backward model). Parameters ---------- %(info_not_none)s Used for fitting. If not available, consider using :func:`mne.create_info`. components : float | array of float | None The patterns to plot. If None, n_components will be shown. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then first available channel type from order given above is used. Defaults to None. vmin : float | callable The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data). If callable, the output equals vmin(data). vmax : float | callable The value specifying the upper bound of the color range. If None, the maximum absolute value is used. If vmin is None, but vmax is not, defaults to np.min(data). If callable, the output equals vmax(data). cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None Colormap to use. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the range. Up and down arrows can be used to change the colormap. If None, 'Reds' is used for all positive data, otherwise defaults to 'RdBu_r'. If 'interactive', translates to (None, True). Defaults to 'RdBu_r'. .. warning:: Interactive mode works smoothly only for a small amount of topomaps. sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be used (via .add_artist). Defaults to True. colorbar : bool Plot a colorbar. scalings : dict | float | None The scalings of the channel types to be applied for plotting. If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``. units : dict | str | None The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined. res : int The resolution of the topomap image (n pixels along each side). size : float Side length per topomap in inches. cbar_fmt : str String format for colorbar values. name_format : str String format for topomap values. Defaults to "CSP%%01d". show : bool Show figure if True. show_names : bool | callable If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix 'MEG ' from all channel names, pass the function lambda x: x.replace('MEG ', ''). If ``mask`` is not None, only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. mask : ndarray of bool, shape (n_channels, n_times) | None The channels to be marked as significant at a given time point. Indices set to `True` will be considered. Defaults to None. mask_params : dict | None Additional plotting parameters for plotting significant sensors. Default (None) equals:: dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markersize=4) %(outlines_topomap)s contours : int | array of float The number of contour lines to draw. If 0, no contours will be drawn. When an integer, matplotlib ticker locator is used to find suitable values for the contour thresholds (may sometimes be inaccurate, use array for accuracy). If an array, the values represent the levels for the contours. Defaults to 6. %(image_interp_topomap)s average : float | None The time window around a given time to be used for averaging (seconds). For example, 0.01 would translate into window that starts 5 ms before and ends 5 ms after a given time point. Defaults to None, which means no averaging. Returns ------- fig : instance of matplotlib.figure.Figure The figure. """ from .. import EvokedArray if components is None: components = np.arange(self.n_components) # set sampling frequency to have 1 component per time point info = cp.deepcopy(info) with info._unlock(): info['sfreq'] = 1. # create an evoked filters = EvokedArray(self.filters_.T, info, tmin=0) # the call plot_topomap return filters.plot_topomap( times=components, ch_type=ch_type, vmin=vmin, vmax=vmax, cmap=cmap, colorbar=colorbar, res=res, cbar_fmt=cbar_fmt, sensors=sensors, scalings=scalings, units=units, time_unit='s', time_format=name_format, size=size, show_names=show_names, title=title, mask_params=mask_params, mask=mask, outlines=outlines, contours=contours, image_interp=image_interp, show=show, average=average) def _compute_covariance_matrices(self, X, y): _, n_channels, _ = X.shape if self.cov_est == "concat": cov_estimator = self._concat_cov elif self.cov_est == "epoch": cov_estimator = self._epoch_cov covs = [] sample_weights = [] for this_class in self._classes: cov, weight = cov_estimator(X[y == this_class]) if self.norm_trace: cov /= np.trace(cov) covs.append(cov) sample_weights.append(weight) return np.stack(covs), np.array(sample_weights) def _concat_cov(self, x_class): """Concatenate epochs before computing the covariance.""" _, n_channels, _ = x_class.shape x_class = np.transpose(x_class, [1, 0, 2]) x_class = x_class.reshape(n_channels, -1) cov = _regularized_covariance( x_class, reg=self.reg, method_params=self.cov_method_params, rank=self.rank) weight = x_class.shape[0] return cov, weight def _epoch_cov(self, x_class): """Mean of per-epoch covariances.""" cov = sum(_regularized_covariance( this_X, reg=self.reg, method_params=self.cov_method_params, rank=self.rank) for this_X in x_class) cov /= len(x_class) weight = len(x_class) return cov, weight def _decompose_covs(self, covs, sample_weights): from scipy import linalg n_classes = len(covs) if n_classes == 2: eigen_values, eigen_vectors = linalg.eigh(covs[0], covs.sum(0)) else: # The multiclass case is adapted from # http://github.com/alexandrebarachant/pyRiemann eigen_vectors, D = _ajd_pham(covs) eigen_vectors = self._normalize_eigenvectors(eigen_vectors.T, covs, sample_weights) eigen_values = None return eigen_vectors, eigen_values def _compute_mutual_info(self, covs, sample_weights, eigen_vectors): class_probas = sample_weights / sample_weights.sum() mutual_info = [] for jj in range(eigen_vectors.shape[1]): aa, bb = 0, 0 for (cov, prob) in zip(covs, class_probas): tmp = np.dot(np.dot(eigen_vectors[:, jj].T, cov), eigen_vectors[:, jj]) aa += prob * np.log(np.sqrt(tmp)) bb += prob * (tmp ** 2 - 1) mi = - (aa + (3.0 / 16) * (bb ** 2)) mutual_info.append(mi) return mutual_info def _normalize_eigenvectors(self, eigen_vectors, covs, sample_weights): # Here we apply an euclidean mean. See pyRiemann for other metrics mean_cov = np.average(covs, axis=0, weights=sample_weights) for ii in range(eigen_vectors.shape[1]): tmp = np.dot(np.dot(eigen_vectors[:, ii].T, mean_cov), eigen_vectors[:, ii]) eigen_vectors[:, ii] /= np.sqrt(tmp) return eigen_vectors def _order_components(self, covs, sample_weights, eigen_vectors, eigen_values, component_order): n_classes = len(self._classes) if component_order == 'mutual_info' and n_classes > 2: mutual_info = self._compute_mutual_info(covs, sample_weights, eigen_vectors) ix = np.argsort(mutual_info)[::-1] elif component_order == 'mutual_info' and n_classes == 2: ix = np.argsort(np.abs(eigen_values - 0.5))[::-1] elif component_order == 'alternate' and n_classes == 2: i = np.argsort(eigen_values) ix = np.empty_like(i) ix[1::2] = i[:len(i) // 2] ix[0::2] = i[len(i) // 2:][::-1] return ix def _ajd_pham(X, eps=1e-6, max_iter=15): """Approximate joint diagonalization based on Pham's algorithm. This is a direct implementation of the PHAM's AJD algorithm [1]. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_channels) A set of covariance matrices to diagonalize. eps : float, default 1e-6 The tolerance for stopping criterion. max_iter : int, default 1000 The maximum number of iteration to reach convergence. Returns ------- V : ndarray, shape (n_channels, n_channels) The diagonalizer. D : ndarray, shape (n_epochs, n_channels, n_channels) The set of quasi diagonal matrices. References ---------- .. [1] Pham, Dinh Tuan. "Joint approximate diagonalization of positive definite Hermitian matrices." SIAM Journal on Matrix Analysis and Applications 22, no. 4 (2001): 1136-1152. """ # Adapted from http://github.com/alexandrebarachant/pyRiemann n_epochs = X.shape[0] # Reshape input matrix A = np.concatenate(X, axis=0).T # Init variables n_times, n_m = A.shape V = np.eye(n_times) epsilon = n_times * (n_times - 1) * eps for it in range(max_iter): decr = 0 for ii in range(1, n_times): for jj in range(ii): Ii = np.arange(ii, n_m, n_times) Ij = np.arange(jj, n_m, n_times) c1 = A[ii, Ii] c2 = A[jj, Ij] g12 = np.mean(A[ii, Ij] / c1) g21 = np.mean(A[ii, Ij] / c2) omega21 = np.mean(c1 / c2) omega12 = np.mean(c2 / c1) omega = np.sqrt(omega12 * omega21) tmp = np.sqrt(omega21 / omega12) tmp1 = (tmp * g12 + g21) / (omega + 1) tmp2 = (tmp * g12 - g21) / max(omega - 1, 1e-9) h12 = tmp1 + tmp2 h21 = np.conj((tmp1 - tmp2) / tmp) decr += n_epochs * (g12 * np.conj(h12) + g21 * h21) / 2.0 tmp = 1 + 1.j * 0.5 * np.imag(h12 * h21) tmp = np.real(tmp + np.sqrt(tmp ** 2 - h12 * h21)) tau = np.array([[1, -h12 / tmp], [-h21 / tmp, 1]]) A[[ii, jj], :] = np.dot(tau, A[[ii, jj], :]) tmp = np.c_[A[:, Ii], A[:, Ij]] tmp = np.reshape(tmp, (n_times * n_epochs, 2), order='F') tmp = np.dot(tmp, tau.T) tmp = np.reshape(tmp, (n_times, n_epochs * 2), order='F') A[:, Ii] = tmp[:, :n_epochs] A[:, Ij] = tmp[:, n_epochs:] V[[ii, jj], :] = np.dot(tau, V[[ii, jj], :]) if decr < epsilon: break D = np.reshape(A, (n_times, -1, n_times)).transpose(1, 0, 2) return V, D @fill_doc class SPoC(CSP): """Implementation of the SPoC spatial filtering. Source Power Comodulation (SPoC) :footcite:`DahneEtAl2014` allows to extract spatial filters and patterns by using a target (continuous) variable in the decomposition process in order to give preference to components whose power correlates with the target variable. SPoC can be seen as an extension of the CSP driven by a continuous variable rather than a discrete variable. Typical applications include extraction of motor patterns using EMG power or audio patterns using sound envelope. Parameters ---------- n_components : int The number of components to decompose M/EEG signals. reg : float | str | None (default None) If not None (same as ``'empirical'``, default), allow regularization for covariance estimation. If float, shrinkage is used (0 <= shrinkage <= 1). For str options, ``reg`` will be passed to ``method`` to :func:`mne.compute_covariance`. log : None | bool (default None) If transform_into == 'average_power' and log is None or True, then applies a log transform to standardize the features, else the features are z-scored. If transform_into == 'csp_space', then log must be None. transform_into : {'average_power', 'csp_space'} If 'average_power' then self.transform will return the average power of each spatial filter. If 'csp_space' self.transform will return the data in CSP space. Defaults to 'average_power'. cov_method_params : dict | None Parameters to pass to :func:`mne.compute_covariance`. .. versionadded:: 0.16 %(rank_none)s .. versionadded:: 0.17 Attributes ---------- filters_ : ndarray, shape (n_channels, n_channels) If fit, the SPoC spatial filters, else None. patterns_ : ndarray, shape (n_channels, n_channels) If fit, the SPoC spatial patterns, else None. mean_ : ndarray, shape (n_components,) If fit, the mean squared power for each component. std_ : ndarray, shape (n_components,) If fit, the std squared power for each component. See Also -------- mne.preprocessing.Xdawn, CSP References ---------- .. footbibliography:: """ def __init__(self, n_components=4, reg=None, log=None, transform_into='average_power', cov_method_params=None, rank=None): """Init of SPoC.""" super(SPoC, self).__init__(n_components=n_components, reg=reg, log=log, cov_est="epoch", norm_trace=False, transform_into=transform_into, rank=rank, cov_method_params=cov_method_params) # Covariance estimation have to be done on the single epoch level, # unlike CSP where covariance estimation can also be achieved through # concatenation of all epochs from the same class. delattr(self, 'cov_est') delattr(self, 'norm_trace') def fit(self, X, y): """Estimate the SPoC decomposition on epochs. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_times) The data on which to estimate the SPoC. y : array, shape (n_epochs,) The class for each epoch. Returns ------- self : instance of SPoC Returns the modified instance. """ from scipy import linalg self._check_Xy(X, y) if len(np.unique(y)) < 2: raise ValueError("y must have at least two distinct values.") # The following code is directly copied from pyRiemann # Normalize target variable target = y.astype(np.float64) target -= target.mean() target /= target.std() n_epochs, n_channels = X.shape[:2] # Estimate single trial covariance covs = np.empty((n_epochs, n_channels, n_channels)) for ii, epoch in enumerate(X): covs[ii] = _regularized_covariance( epoch, reg=self.reg, method_params=self.cov_method_params, rank=self.rank) C = covs.mean(0) Cz = np.mean(covs * target[:, np.newaxis, np.newaxis], axis=0) # solve eigenvalue decomposition evals, evecs = linalg.eigh(Cz, C) evals = evals.real evecs = evecs.real # sort vectors ix = np.argsort(np.abs(evals))[::-1] # sort eigenvectors evecs = evecs[:, ix].T # spatial patterns self.patterns_ = linalg.pinv(evecs).T # n_channels x n_channels self.filters_ = evecs # n_channels x n_channels pick_filters = self.filters_[:self.n_components] X = np.asarray([np.dot(pick_filters, epoch) for epoch in X]) # compute features (mean band power) X = (X ** 2).mean(axis=-1) # To standardize features self.mean_ = X.mean(axis=0) self.std_ = X.std(axis=0) return self def transform(self, X): """Estimate epochs sources given the SPoC filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data. Returns ------- X : ndarray If self.transform_into == 'average_power' then returns the power of CSP features averaged over time and shape (n_epochs, n_sources) If self.transform_into == 'csp_space' then returns the data in CSP space and shape is (n_epochs, n_sources, n_times). """ return super(SPoC, self).transform(X)