def subject_independent_setting(k_folds,
pick_smp_freq,
n_components,
n_features,
bands,
order,
save_path,
num_class=2,
sel_chs=None):
sel_chs = CONSTANT['sel_chs'] if sel_chs == None else sel_chs
n_folds = k_folds
save_path = save_path + '/BCIC2a/fbcsp/{}_class/subject_independent'.format(
num_class)
n_chs = len(sel_chs)
n_trials = n_trials_per_class * num_class
X_train_all, y_train_all = np.zeros(
(n_subjs, n_trials, n_chs, int(MI_len * pick_smp_freq))), np.zeros(
(n_subjs, n_trials))
X_test_all, y_test_all = np.zeros(
(n_subjs, n_trials, n_chs, int(MI_len * pick_smp_freq))), np.zeros(
(n_subjs, n_trials))
id_chosen_chs = raw.chanel_selection(sel_chs)
for s in range(n_subjs):
X_train, y_train, X_test, y_test = __load_BCIC2a(
raw_path, s + 1, pick_smp_freq, num_class, id_chosen_chs)
X_train_all[s], y_train_all[s] = X_train, y_train
X_test_all[s], y_test_all[s] = X_test, y_test
for directory in [save_path]:
if not os.path.exists(directory):
os.makedirs(directory)
# Carry out subject-independent setting with 5-fold cross validation
for person, (X_val, y_val, X_te, y_te) in enumerate(
zip(X_train_all, y_train_all, X_test_all, y_test_all)):
train_subj = [i for i in range(n_subjs)]
train_subj = np.delete(train_subj, person) # remove test subject
# Generating fake data to used for k-fold cross-validation only
fake_tr = np.zeros((len(train_subj), 2))
fake_tr_la = np.zeros((len(train_subj)))
skf = StratifiedKFold(n_splits=n_folds, random_state=42, shuffle=True)
for fold, (train_ind,
val_ind) in enumerate(skf.split(fake_tr, fake_tr_la)):
print('FOLD:', fold + 1, 'TRAIN:', len(train_ind), 'VALIDATION:',
len(val_ind))
train_index, val_index = train_subj[train_ind], train_subj[val_ind]
X_train_cat = np.concatenate(
(X_train_all[train_index], X_test_all[train_index]), axis=0)
X_val_cat = np.concatenate(
(X_train_all[val_index], X_test_all[val_index]), axis=0)
y_train_cat = np.concatenate(
(y_train_all[train_index], y_test_all[train_index]), axis=0)
y_val_cat = np.concatenate(
(y_train_all[val_index], y_test_all[val_index]), axis=0)
X_train = X_train_cat.reshape(-1, X_train_cat.shape[2],
X_train_cat.shape[3])
y_train = y_train_cat.reshape(-1)
X_val = X_val_cat.reshape(-1, X_val_cat.shape[2],
X_val_cat.shape[3])
y_val = y_val_cat.reshape(-1)
X_test = X_te
y_test = y_te
# Peforming FBCSP feature extraction
fbcsp_scaler = FBCSP(bands=bands,
smp_freq=pick_smp_freq,
num_class=num_class,
order=order,
n_components=n_components,
n_features=n_features)
X_train_fbcsp = fbcsp_scaler.fit_transform(X_train, y_train)
X_val_fbcsp = fbcsp_scaler.transform(X_val)
X_test_fbcsp = fbcsp_scaler.transform(X_test)
print(
"Check dimension of training data {}, val data {} and testing data {}"
.format(X_train_fbcsp.shape, X_val_fbcsp.shape,
X_test_fbcsp.shape))
SAVE_NAME = 'S{:03d}_fold{:03d}'.format(person + 1, fold + 1)
__save_data_with_valset(save_path, SAVE_NAME, X_train_fbcsp,
y_train, X_val_fbcsp, y_val, X_test_fbcsp,
y_test)
print('The preprocessing of subject {} from fold {} is DONE!!!'.
format(person + 1, fold + 1))
def subject_dependent_setting(k_folds,
pick_smp_freq,
n_components,
n_features,
bands,
order,
save_path,
num_class=2,
sel_chs=None):
sel_chs = CONSTANT['sel_chs'] if sel_chs == None else sel_chs
n_folds = k_folds
save_path = save_path + '/BCIC2a/fbcsp/{}_class/subject_dependent'.format(
num_class)
n_chs = len(sel_chs)
n_trials = n_trials_per_class * num_class
X_train_all, y_train_all = np.zeros(
(n_subjs, n_trials, n_chs, int(MI_len * pick_smp_freq))), np.zeros(
(n_subjs, n_trials))
X_test_all, y_test_all = np.zeros(
(n_subjs, n_trials, n_chs, int(MI_len * pick_smp_freq))), np.zeros(
(n_subjs, n_trials))
id_chosen_chs = raw.chanel_selection(sel_chs)
for s in range(n_subjs):
X_train, y_train, X_test, y_test = __load_BCIC2a(
raw_path, s + 1, pick_smp_freq, num_class, id_chosen_chs)
X_train_all[s], y_train_all[s] = X_train, y_train
X_test_all[s], y_test_all[s] = X_test, y_test
for directory in [save_path]:
if not os.path.exists(directory):
os.makedirs(directory)
# Carry out subject-dependent setting with 5-fold cross validation
for person, (X_tr, y_tr, X_te, y_te) in enumerate(
zip(X_train_all, y_train_all, X_test_all, y_test_all)):
if len(X_tr.shape) != 3:
raise Exception('Dimension Error, must have 3 dimension')
skf = StratifiedKFold(n_splits=n_folds, random_state=42, shuffle=True)
for fold, (train_index, val_index) in enumerate(skf.split(X_tr, y_tr)):
print('FOLD:', fold + 1, 'TRAIN:', len(train_index), 'VALIDATION:',
len(val_index))
X_tr_cv, X_val_cv = X_tr[train_index], X_tr[val_index]
y_tr_cv, y_val_cv = y_tr[train_index], y_tr[val_index]
# Peforming FBCSP feature extraction
fbcsp_scaler = FBCSP(bands=bands,
smp_freq=pick_smp_freq,
num_class=num_class,
order=order,
n_components=n_components,
n_features=n_features)
X_tr_fbcsp = fbcsp_scaler.fit_transform(X_tr_cv, y_tr_cv)
X_val_fbcsp = fbcsp_scaler.transform(X_val_cv)
X_te_fbcsp = fbcsp_scaler.transform(X_te)
print(
'Check dimension of training data {}, val data {} and testing data {}'
.format(X_tr_fbcsp.shape, X_val_fbcsp.shape, X_te_fbcsp.shape))
SAVE_NAME = 'S{:03d}_fold{:03d}'.format(person + 1, fold + 1)
__save_data_with_valset(save_path, SAVE_NAME, X_tr_fbcsp, y_tr_cv,
X_val_fbcsp, y_val_cv, X_te_fbcsp, y_te)
print('The preprocessing of subject {} from fold {} is DONE!!!'.
format(person + 1, fold + 1))