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 test_from_cross_template_P300(template_path,
subject_path,
test_chnames,
flashmode='RoCo',
nb_targets=180,
visu=False):
T = 0
NT = 1
ERP = np.load(template_path + 'ERP_Array.npy')
Centroids_List = np.load(template_path + 'Centroids_List.npy')
mu_TNT = np.load(template_path + 'rTNT_mu.npy')
sigma_TNT = np.load(template_path + 'rTNT_var.npy')
data, labels, event = get_data_from_csv_EIV(
myfilename=subject_path + '-signals.csv',
markersfile=subject_path + 'markers.csv',
chnames=test_chnames)
erp = ERPCovariances()
erp.P = ERP
erp.estimator = 'cov'
X = erp.transform(data)
train_NaiveBayes = R_TNT_NaiveBayes(targets=[
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '1', '2',
'3', '4', '5', '6', '7', '8', '9', '_'
],
mu_TNT=mu_TNT,
sigma_TNT=sigma_TNT,
class_prior=None)
dist = [
np.array([distance(x, Centroids_List[l]) for i, x in enumerate(X)])
for l in [T, NT]
]
r_TNT = np.array(np.log(dist[0] / dist[1]))
mean, var = test_loop_P300(r_TNT_test=r_TNT,
y_test=labels,
e_test=event,
train_NaiveBayes=train_NaiveBayes,
T=0,
NT=1,
flashmode=flashmode,
visu=visu,
nb_targets=nb_targets)
return mean, var
def distribution_single_from_template(test_sub, test_sess, template_path,
database):
x_test, y_test, e_test, t_test, _, _ = database.subjects[
test_sub].get_data([test_sess])
ERP_train = np.load(template_path + 'ERP_Array.npy')
erp_train = ERPCovariances(estimator='cov')
erp_train.P = ERP_train
X_test = erp_train.transform(x_test)
centroids_train = np.load(template_path + 'Centroids_List.npy')
erp_train = ERPCovariances(estimator='cov')
erp_train.P = ERP_train
r_TNT_test = predict_R_TNT(X=X_test, centroids_list=centroids_train)
return r_TNT_test, y_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
df[tmax] = pd.DataFrame(scores, columns=['subject', 'VR', 'PC'])