def gen_tscsp(sub='1'):
path = 'data/A0' + sub + 'T.npz'
cnt, mrk, dur, y = Competition.load_cnt_mrk_y(path)
epo = []
for i in range(1, 10): # band-pass filtering
cnt_fs = CSP.arr_bandpass_filter(cnt, i * 4, (i + 1) * 4, 250, 4)
cur_epo = cnt_to_epo(cnt_fs, mrk, dur)
cur_epo, y_ = out_label_remover(cur_epo, y)
epo.append(cur_epo)
y = BCI.lab_inv_translator(y_, 4)
kv = BCI.gen_kv_idx(y, 5)
k = 1
for train_idx, test_idx in kv:
train_x = epo_temporal_dividing(
np.array(epo)[:, train_idx, :, :], 4, 0.5, 250)
test_x = epo_temporal_dividing(
np.array(epo)[:, test_idx, :, :], 4, 0.5, 250)
train_y = np.transpose(np.array(y)[train_idx])
test_y = np.transpose(np.array(y)[test_idx])
res = {
'train_x': train_x,
'test_x': test_x,
'train_y': train_y,
'test_y': test_y
}
scipy.io.savemat(
'competition/ori_4_0.5/' + sub + '_' + str(k) + '.mat', res)
k += 1
def make_data_temporal(sub):
cnt = scipy.io.loadmat('raw_KIST/twist/raw_cnt_' + sub +
'.mat')['cnt'][0][0][4]
mrk = scipy.io.loadmat('raw_KIST/twist/raw_mrk_' + sub +
'.mat')['mrk'][0][0][0][0]
y = scipy.io.loadmat('raw_KIST/twist/raw_mrk_' + sub +
'.mat')['mrk'][0][0][3]
epo = []
for i in range(1, 10):
cnt_fs = CSP.arr_bandpass_filter(cnt, i * 4, (i + 1) * 4, 1000, 4)
epo.append(cnt_to_epo(cnt_fs, mrk))
y_ = np.transpose(y)
kv = BCI.gen_kv_idx(y_, 5)
k = 1
for train_idx, test_idx in kv:
train_x = epo_temporal_dividing(
np.array(epo)[:, train_idx, :, :], 0.6, 0.3)
test_x = epo_temporal_dividing(
np.array(epo)[:, test_idx, :, :], 0.6, 0.3)
train_y = y[:, train_idx]
test_y = y[:, test_idx]
res = {
'train_x': train_x,
'test_x': test_x,
'train_y': train_y,
'test_y': test_y
}
scipy.io.savemat('RCNN2/twist_ori_dj/' + sub + '_' + str(k) + '.mat',
res)
k += 1
def data_load(file, filtering=True):
path = 'E:/Richard/EEG/Competition/raw/'
cnt, mrk, dur, y = com.load_cnt_mrk_y(path + file)
if filtering:
cnt = csp.arr_bandpass_filter(cnt, 8, 30, 250, 5)
epo = com.cnt_to_epo(cnt, mrk, dur)
epo, y = com.out_label_remover(epo, y)
return epo, y
def gen_fbcsp(path='data/A01T.npz'):
x, y = Competition.load_one_data(path)
for i in range(1, 10):
print(i)
x_ = CSP.arr_bandpass_filter(x, i * 4, (i + 1) * 4, 250)
f = one_fbcsp(x_, y)
scipy.io.savemat('mat/fbcsp_ori/A01T_' + str(i) + '_1.mat', f[0])
scipy.io.savemat('mat/fbcsp_ori/A01T_' + str(i) + '_2.mat', f[1])
scipy.io.savemat('mat/fbcsp_ori/A01T_' + str(i) + '_3.mat', f[2])
def gen_cnt_data():
import os
os.chdir('E:/Richard/Competition/')
for i in range(1, 10):
path = 'raw/A0' + str(i) + 'T.npz'
cnt, mrk, dur, y = Competition.load_cnt_mrk_y(path)
f_cnt = CSP.arr_bandpass_filter(cnt, 8, 30, 250, 5)
epo = cnt_to_epo(f_cnt, mrk, dur)
epo, y_ = out_label_remover(epo, y)
new_epo = []
for v in epo:
new_epo.append(v[750:1500, :])
new_epo = np.array(new_epo)
data = {'x': new_epo, 'y': y_}
scipy.io.savemat('epo_f/A0' + str(i), data)
def load_kist_data(sub = '3'):
import pickle
print(sub)
x = scipy.io.loadmat('kist_data/grasp/x_' + sub + '.mat')['x_' + sub].transpose()
y = scipy.io.loadmat('kist_data/grasp/y_' + sub + '.mat')['y_' + sub].transpose().argmax(axis=1)
y_ = np.array(BCI.lab_inv_translator(y))
kv = BCI.gen_kv_idx(y_, 5)
k = 1
for train_idx, test_idx in kv:
x_train, y_train = x[train_idx], y_[train_idx]
x_test, y_test = x[test_idx], y_[test_idx]
file = open('kist_data/grasp/np/' + sub + '_' + str(k) + '.pic', 'wb')
pickle.dump({'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test}, file)
file.close()
step = int(len(x[0][0]) / 5)
for i in range(0, 5):
cur_x_train = x_train[:, :, step * i: step * (i + 1)]
cur_x_test = x_test[:, :, step * i: step * (i + 1)]
for j in range(1, 10):
cur_cur_x_train = CSP.arr_bandpass_filter(cur_x_train, j*4, (j+1)*4, 250)
cur_cur_x_test = CSP.arr_bandpass_filter(cur_x_test, j*4, (j+1)*4, 250)
f_train = one_fbcsp_for_kist(cur_cur_x_train, y_train.argmax(axis=1))
f_test = one_fbcsp_for_kist(cur_cur_x_test, y_test.argmax(axis=1))
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_1_' + str(k) + '_train.mat', f_train[0])
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_2_' + str(k) + '_train.mat', f_train[1])
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_3_' + str(k) + '_train.mat', f_train[2])
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_1_' + str(k) + '_test.mat', f_test[0])
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_2_' + str(k) + '_test.mat', f_test[1])
scipy.io.savemat('mat/kist_ori/grasp/A0' + sub + 'T_' + str(i) + '_' + str(j) + '_3_' + str(k) + '_test.mat', f_test[2])
k += 1
def gen_tscsp_trick(sub):
path = 'data/A0' + sub + 'T.npz'
cnt, mrk, dur, y = Competition.load_cnt_mrk_y(path)
epo = []
for i in range(1, 10): # band-pass filtering
cnt_fs = CSP.arr_bandpass_filter(cnt, i * 4, (i + 1) * 4, 250, 4)
cur_epo = cnt_to_epo(cnt_fs, mrk, dur)
cur_epo, y_ = out_label_remover(cur_epo, y)
epo.append(cur_epo)
y = BCI.lab_inv_translator(y_, 4)
kv = BCI.gen_kv_idx(y, 5)
k = 1
for train_idx, test_idx in kv:
train_x = epo_temporal_dividing(
np.array(epo)[:, train_idx, :, :], 3.5, 0.5, 250)
test_x = epo_temporal_dividing(
np.array(epo)[:, test_idx, :, :], 3.5, 0.5, 250)
cls = trick_ori(train_x, test_x,
np.array(y)[train_idx].argmax(axis=1),
np.array(y)[test_idx].argmax(axis=1))
scipy.io.savemat(
'competition/trick/ori_3.5_0.5/' + sub + '_' + str(k) + '.mat',
{'cls': cls})
k += 1
def gen_ts_topo(sub='1'):
optimal_bank = [20, 30, 19, 9, 9, 20, 20, 19, 22]
path = 'data/A0' + sub + 'T.npz'
x, y = Competition.load_one_data(path)
x = np.array(x)
step = int(len(x[0][0]) / 5)
for i in range(0, 5):
cur_x = x[:, :, step * i:step * (i + 1)]
for j in range(1, 10):
optimal_idx = optimal_bank[int(sub) - 1]
pred_j = optimal_idx % 9
pred_i = int(optimal_idx / 9)
if pred_i == i and pred_j == j - 1:
lab_1 = {
'clab': [
'Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3',
'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz',
'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'
],
'fs':
250,
'x': [],
'y': []
}
lab_2 = {
'clab': [
'Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3',
'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz',
'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'
],
'fs':
250,
'x': [],
'y': []
}
lab_3 = {
'clab': [
'Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3',
'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz',
'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'
],
'fs':
250,
'x': [],
'y': []
}
lab_4 = {
'clab': [
'Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3',
'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz',
'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'
],
'fs':
250,
'x': [],
'y': []
}
cur_cur_x = CSP.arr_bandpass_filter(cur_x, j * 4, (j + 1) * 4,
250)
for k in range(len(y)):
if y[k] == 0:
lab_1['x'].append(np.transpose(cur_cur_x[k]))
lab_1['y'].append([1, 0, 0, 0])
elif y[k] == 1:
lab_2['x'].append(np.transpose(cur_cur_x[k]))
lab_2['y'].append([0, 1, 0, 0])
elif y[k] == 2:
lab_3['x'].append(np.transpose(cur_cur_x[k]))
lab_3['y'].append([0, 0, 1, 0])
elif y[k] == 3:
lab_4['x'].append(np.transpose(cur_cur_x[i]))
lab_4['y'].append([0, 0, 0, 1])
scipy.io.savemat('mat/topo_ori/A0' + sub + 'T_1.mat', lab_1)
scipy.io.savemat('mat/topo_ori/A0' + sub + 'T_2.mat', lab_2)
scipy.io.savemat('mat/topo_ori/A0' + sub + 'T_3.mat', lab_3)
scipy.io.savemat('mat/topo_ori/A0' + sub + 'T_4.mat', lab_4)
print('abc')
