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data_1d.py
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data_1d.py
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'''
Date: 2021-07-13 23:37:15
LastEditors: Chenhuiyu
LastEditTime: 2021-08-05 20:30:39
FilePath: \\2021-07-AttenEmotionNet\\data_processing\\generate_npy.py
'''
import os
import scipy.io
import numpy as np
def awgn(x, snr=30):
'''
加入高斯白噪声 Additive White Gaussian Noise
:param x: 原始信号
:param snr: 信噪比
:return: 加入噪声后的信号
'''
seed = np.random.randint(0, 955)
np.random.seed(seed) # 设置随机种子
snr = 10**(snr / 10.0)
xpower = np.sum(x**2) / len(x)
npower = xpower / snr
noise = np.random.randn(x.shape[0], x.shape[1], x.shape[2]) * np.sqrt(npower)
return x + noise
def standardization(data):
mu = np.mean(data)
sigma = np.std(data)
return (data - mu) / sigma
def map_to_2d(data_1d):
data_2d = np.zeros((data_1d.shape[0], 9, 9, 5))
data_2d[:, 0, 3:6, :] = data_1d[:, 0:3, :]
data_2d[:, 1, 3, :] = data_1d[:, 3, :]
data_2d[:, 1, 5, :] = data_1d[:, 4, :]
data_2d[:, 2, :, :] = data_1d[:, 5:14, :]
data_2d[:, 3, :, :] = data_1d[:, 14:23, :]
data_2d[:, 4, :, :] = data_1d[:, 23:32, :]
data_2d[:, 5, :, :] = data_1d[:, 32:41, :]
data_2d[:, 6, :, :] = data_1d[:, 41:50, :]
data_2d[:, 7, 1:8, :] = data_1d[:, 50:57, :]
data_2d[:, 8, 2:7, :] = data_1d[:, 57:62, :]
return data_2d
dataset_path = 'E:/dataset/EEG-Emotion/SEED/ExtractedFeatures'
label_file = os.path.join(dataset_path, 'label.mat')
label = scipy.io.loadmat(label_file)
label_dict = label['label'].squeeze() + 1
# print(label)
train_data = []
train_label = []
test_data = []
test_label = []
val_data = []
val_label = []
for j, file in enumerate(os.listdir(dataset_path)):
if len(file.split('_')) == 2:
# 第i个被试
subject_i = int(file.split('_')[0])
# 每个被试有3个section
section_j = j % 3
print('subject:', subject_i, ' section:', section_j, file)
# 读取被试subject_i第section_j次实验的数据，其中包含15个trails
data = scipy.io.loadmat(os.path.join(dataset_path, file))
# 对每个trail进行处理
# trail_id = 1
for trail_id in range(1, 16):
for key in data.keys():
if key == 'de_LDS' + str(trail_id):
# print(key)
# 读取de特征值，shape(62,2XX,5)
# 表示62个导联数，2xx个时间点，5个频带
de_data = data[key]
CropLength = 25
# if section_j == 0:
if subject_i == 10:
# if subject_i % 2 == 0:
data_1d = de_data.transpose((1, 0, 2))
data_1d = standardization(data_1d)
label = label_dict[trail_id - 1]
for i in range(0, len(data_1d), CropLength):
if i + CropLength > len(data_1d):
break
val_data.append(data_1d[i:i + CropLength].transpose(2, 0, 1))
val_label.append(label)
elif subject_i == 9:
data_1d = de_data.transpose((1, 0, 2))
data_1d = standardization(data_1d)
label = label_dict[trail_id - 1]
for i in range(0, len(data_1d), CropLength):
if i + CropLength > len(data_1d):
break
test_data.append(data_1d[i:i + CropLength].transpose(2, 0, 1))
test_label.append(label)
else:
data_1d = de_data.transpose((1, 0, 2))
data_1d = standardization(data_1d)
label = label_dict[trail_id - 1]
for i in range(0, len(data_1d), CropLength):
if i + CropLength > len(data_1d):
break
train_data.append(data_1d[i:i + CropLength].transpose(2, 0, 1))
train_label.append(label)
# for i in range(5):
# data_1d = de_data.transpose((1, 0, 2))
# data_1d = standardization(data_1d)
# data_1d = awgn(data_1d, snr=30)
# data_2d = map_to_2d(data_1d)
# label = label_dict[trail_id - 1]
# for i in range(0, len(data_2d), CropLength):
# if i + CropLength > len(data_2d):
# break
# train_data.append(data_2d[i:i + CropLength])
# train_label.append(label)
train_data = np.array(train_data)
train_label = np.array(train_label)
val_data = np.array(val_data)
val_label = np.array(val_label)
test_data = np.array(test_data)
test_label = np.array(test_label)
data_path = ".\\input_data_1d"
np.save(os.path.join(data_path, "train_data.npy"), train_data)
np.save(os.path.join(data_path, "train_label.npy"), train_label)
np.save(os.path.join(data_path, "val_data.npy"), val_data)
np.save(os.path.join(data_path, "val_label.npy"), val_label)
np.save(os.path.join(data_path, "test_data.npy"), test_data)
np.save(os.path.join(data_path, "test_label.npy"), test_label)