def erp_cov_vr_pc(X_training, labels_training, X_test, labels_test, class_name,
class_info):
# estimate the extended ERP covariance matrices with Xdawn
erpc = ERPCovariances(classes=[class_info[class_name]], estimator='lwf')
erpc.fit(X_training, labels_training)
covs_training = erpc.transform(X_training)
covs_test = erpc.transform(X_test)
# get the AUC for the classification
clf = MDM()
clf.fit(covs_training, labels_training)
labels_pred = clf.predict(covs_test)
return roc_auc_score(labels_test, labels_pred)
def get_sourcetarget_split_p300(source, target, ncovs_train):
X_source = source['epochs']
y_source = source['labels'].flatten()
covs_source = ERPCovariances(classes=[2], estimator='lwf').fit_transform(
X_source, y_source)
source = {}
source['covs'] = covs_source
source['labels'] = y_source
X_target = target['epochs']
y_target = target['labels'].flatten()
sel = np.arange(len(y_target))
np.random.shuffle(sel)
X_target = X_target[sel]
y_target = y_target[sel]
idx_erps = np.where(y_target == 2)[0][:ncovs_train]
idx_rest = np.where(
y_target == 1)[0][:ncovs_train *
5] # because there's one ERP in every 6 flashes
idx_train = np.concatenate([idx_erps, idx_rest])
idx_test = np.array(
[i for i in range(len(y_target)) if i not in idx_train])
erp = ERPCovariances(classes=[2], estimator='lwf')
erp.fit(X_target[idx_train], y_target[idx_train])
target_train = {}
covs_target_train = erp.transform(X_target[idx_train])
y_target_train = y_target[idx_train]
target_train['covs'] = covs_target_train
target_train['labels'] = y_target_train
target_test = {}
covs_target_test = erp.transform(X_target[idx_test])
y_target_test = y_target[idx_test]
target_test['covs'] = covs_target_test
target_test['labels'] = y_target_test
return source, target_train, target_test
def predict_ERP_centroids(x, y, metric='riemann', ERP_bloc=None, T=0, NT=1):
"""Helper to predict the r_TNT for a new set of trials.
Parameters
----------
x : ndarray, shape (n_trials, n_channels, n_times)
y : ndarray, shape (,n_trials)
ERP_bloc : list with 0 or 1 for the class in the ERP
Returns
-------
erp : the ERPCovariance object with erp.P an ndarray, shape (n_channels*len(ERP_bloc), n_times)
centroids : list of the two centers of classe which are both ndarray, shape (n_channels*len(ERP_bloc), n_channels*len(ERP_bloc))
X : ndarray, shape (n_trials, n_channels*len(ERP_bloc), n_channels*len(ERP_bloc)), the set of super covariance matrices of set signals given in input
"""
classes = [T, NT]
erp = ERPCovariances(classes=ERP_bloc, estimator='cov')
erp.fit(X=x, y=y)
X = erp.transform(X=x)
centroids = [
mean_covariance(X[y == l, :, :], metric=metric) for l in classes
]
return erp, centroids, X
kf = KFold(n_splits = 6)
repetitions = [1, 2]
auc = []
blocks = np.arange(1, 12+1)
for train_idx, test_idx in kf.split(np.arange(12)):
# split in training and testing blocks
X_training, labels_training, _ = get_block_repetition(X, labels, meta, blocks[train_idx], repetitions)
X_test, labels_test, _ = get_block_repetition(X, labels, meta, blocks[test_idx], repetitions)
# estimate the extended ERP covariance matrices with Xdawn
dict_labels = {'Target':1, 'NonTarget':0}
erpc = ERPCovariances(classes=[dict_labels['Target']], estimator='lwf')
erpc.fit(X_training, labels_training)
covs_training = erpc.transform(X_training)
covs_test = erpc.transform(X_test)
# get the AUC for the classification
clf = MDM()
clf.fit(covs_training, labels_training)
labels_pred = clf.predict(covs_test)
auc.append(roc_auc_score(labels_test, labels_pred))
# stock scores
scores_subject.append(np.mean(auc))
scores.append(scores_subject)
# print results