# -*- coding: utf-8 -*-
"""
Created on Wed Jul 8 22:00:08 2015.
@author: rc, alexandre
"""
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from mne.io import RawArray
from mne.channels import read_montage
from mne import create_info
from mne import find_events, Epochs
from mne.preprocessing import Xdawn
from mne import compute_raw_data_covariance
from pyriemann.utils.covariance import _lwf
from pyriemann.classification import MDM
from preprocessing.aux import getChannelNames, getEventNames, sliding_window
def toMNE(X, y=None):
"""Tranform array into MNE for epoching."""
ch_names = getChannelNames()
montage = read_montage('standard_1005', ch_names)
ch_type = ['eeg']*len(ch_names)
data = X.T
if y is not None:
y = y.transpose()
ch_type.extend(['stim']*6)
event_names = getEventNames()
ch_names.extend(event_names)
# concatenate event file and data
data = np.concatenate((data, y))
info = create_info(ch_names, sfreq=500.0, ch_types=ch_type,
montage=montage)
raw = RawArray(data, info, verbose=False)
return raw
def get_epochs_and_cov(X, y, window=500):
"""return epochs from array."""
raw_train = toMNE(X, y)
picks = range(len(getChannelNames()))
events = list()
events_id = dict()
for j, eid in enumerate(getEventNames()):
tmp = find_events(raw_train, stim_channel=eid, verbose=False)
tmp[:, -1] = j + 1
events.append(tmp)
events_id[eid] = j + 1
# concatenate and sort events
events = np.concatenate(events, axis=0)
order_ev = np.argsort(events[:, 0])
events = events[order_ev]
epochs = Epochs(raw_train, events, events_id,
tmin=-(window / 500.0) + 1 / 500.0 + 0.150,
tmax=0.150, proj=False, picks=picks, baseline=None,
preload=True, add_eeg_ref=False, verbose=False)
cov_signal = compute_raw_data_covariance(raw_train, verbose=False)
return epochs, cov_signal
class ERP(BaseEstimator, TransformerMixin):
"""ERP cov estimator.
This is a transformer for estimating special form covariance matrices in
the context of ERP detection [1,2]. For each class, the ERP is estimated by
averaging all the epochs of a given class. The dimentionality of the ERP is
reduced using a XDAWN algorithm and then concatenated with the epochs
before estimation of the covariance matrices.
References :
[1] A. Barachant, M. Congedo ,"A Plug&Play P300 BCI Using Information
Geometry", arXiv:1409.0107
[2] M. Congedo, A. Barachant, A. Andreev ,"A New generation of
Brain-Computer Interface Based on Riemannian Geometry", arXiv: 1310.8115.
"""
def __init__(self, window=500, nfilters=3, subsample=1):
"""Init."""
self.window = window
self.nfilters = nfilters
self.subsample = subsample
def fit(self, X, y):
"""fit."""
self._fit(X, y)
return self
def _fit(self, X, y):
"""fit and return epochs."""
epochs, cov_signal = get_epochs_and_cov(X, y, self.window)
xd = Xdawn(n_components=self.nfilters, signal_cov=cov_signal,
correct_overlap=False)
xd.fit(epochs)
P = []
for eid in getEventNames():
P.append(np.dot(xd.filters_[eid][:, 0:self.nfilters].T,
xd.evokeds_[eid].data))
self.P = np.concatenate(P, axis=0)
self.labels_train = epochs.events[:, -1]
return epochs
def transform(self, X, y=None):
"""Transform."""
test_cov = sliding_window(X.T, window=self.window,
subsample=self.subsample,
estimator=self.erp_cov)
return test_cov
def fit_transform(self, X, y):
"""Fit and transform."""
epochs = self._fit(X, y)
train_cov = np.array([self.erp_cov(ep) for ep in epochs.get_data()])
return train_cov
def erp_cov(self, X):
"""Compute ERP covariances."""
data = np.concatenate((self.P, X), axis=0)
return _lwf(data)
def update_subsample(self, old_sub, new_sub):
"""update subsampling."""
self.subsample = new_sub
class ERPDistance(BaseEstimator, TransformerMixin):
"""ERP distance cov estimator.
This transformer estimates Riemannian distance for ERP covariance matrices.
After estimation of special form ERP covariance matrices using the ERP
transformer, a MDM [1] algorithm is used to compute Riemannian distance.
References:
[1] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass
Brain-Computer Interface Classification by Riemannian Geometry," in IEEE
Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012
"""
def __init__(self, window=500, nfilters=3, subsample=1, metric='riemann',
n_jobs=1):
"""Init."""
self.window = window
self.nfilters = nfilters
self.subsample = subsample
self.metric = metric
self.n_jobs = n_jobs
self._fitted = False
def fit(self, X, y):
"""fit."""
# Create ERP and get cov mat
self.ERP = ERP(self.window, self.nfilters, self.subsample)
train_cov = self.ERP.fit_transform(X, y)
labels_train = self.ERP.labels_train
# Add rest epochs
rest_cov = self._get_rest_cov(X, y)
train_cov = np.concatenate((train_cov, rest_cov), axis=0)
labels_train = np.concatenate((labels_train, [0] * len(rest_cov)))
# fit MDM
self.MDM = MDM(metric=self.metric, n_jobs=self.n_jobs)
self.MDM.fit(train_cov, labels_train)
self._fitted = True
return self
def transform(self, X, y=None):
"""Transform."""
test_cov = self.ERP.transform(X)
dist = self.MDM.transform(test_cov)
dist = dist[:, 1:] - np.atleast_2d(dist[:, 0]).T
return dist
def update_subsample(self, old_sub, new_sub):
"""update subsampling."""
if self._fitted:
self.ERP.update_subsample(old_sub, new_sub)
def _get_rest_cov(self, X, y):
"""Sample rest epochs from data and compute the cov mat."""
ix = np.where(np.diff(y[:, 0]) == 1)[0]
rest = []
offset = - self.window
for i in ix:
start = i + offset - self.window
stop = i + offset
rest.append(self.ERP.erp_cov(X[slice(start, stop)].T))
return np.array(rest)
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