目录
关于
工具
数据集
数据集简述
方法实现
数据读取
编辑数据预处理
传统机器学习模型(逻辑回归，支持向量机，随机森林)
多层感知机模型
CNN+transfomer模型
代码获取
关于
- 本实验利用结合了卷积神经网络 (CNN) 和 Transformer 组件的混合架构，实现基于 EEG 的有效情绪分类。
- 尝试各种机器学习模型，包括逻辑回归、支持向量机 (SVM)、随机森林分类器和多层感知器 (MLP) 神经网络，以比较不同模型的性能。
图片来自于： https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9991178
工具
数据集
数据集简述
脑电图数据是从两名受试者（1 名男性、1 名女性，年龄 20-22 岁）收集的，针对特定电影剪辑引发的六种情绪状态（积极、消极、中性）中的每一种状态。该数据集包括从脑电波中收集的 324,000 个数据点，这些数据点被重新采样到 150Hz。还收集了中性脑电波数据，作为代表受试者静息情绪状态的第三类数据。从四个电极（TP9、AF7、AF8、TP10）记录 EEG 数据，并进行处理以生成通过 1 秒滑动窗口提取的统计特征数据集。
图片来源：https://www.researchgate.net/figure/This-figure-shows-the-standard-locations-for-measuring-EEG-as-per-10-20-International_fig2_358644174
方法实现
数据读取
raw_eeg_data = pd.read_csv('../data/features_raw.csv')
raw_eeg_data.head()# plot the F8 column
plt.figure(figsize=(20, 5))
plt.plot(raw_eeg_data['F8'])
plt.title('F8 Electrode Data')
plt.ylabel('Voltage (uV)')
plt.xlabel('Time')
plt.show()# plot the F7 column
plt.figure(figsize=(20, 5))
plt.plot(raw_eeg_data['F7'])
plt.title('F7 Electrode Data')
plt.ylabel('Voltage (uV)')
plt.xlabel('Time')
plt.show()
数据预处理
X = eeg_emotions_data.drop(['label'], axis=1)
y = eeg_emotions_data['label']# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoderlabelencoder_emotions = LabelEncoder()
y = labelencoder_emotions.fit_transform(y)# Standardizing the features in the dataset
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()X = scaler.fit_transform(X)传统机器学习模型(逻辑回归，支持向量机，随机森林)
from sklearn.linear_model import LogisticRegression
import pickle# Create a logistic regression classifier
model = LogisticRegression(random_state=2003, multi_class='multinomial', max_iter=1000)# Train the model
model.fit(X_train, y_train)# Evaluate the model
evaluate_model(y_test, model.predict(X_test))
from sklearn.svm import SVC# Create a model: a support vector classifier
model = SVC(kernel='rbf', gamma='auto', C=1.0, random_state=2003)# Train the model
model.fit(X_train, y_train)# Evaluate the model
evaluate_model(y_test, model.predict(X_test))
from sklearn.ensemble import RandomForestClassifier# Create a random forest Classifier.
model = RandomForestClassifier(n_estimators=100, random_state=2003)# Train the model
model.fit(X_train, y_train)# Evaluate the model
evaluate_model(y_test, model.predict(X_test))
在传统机器模型，我们可以发现随机森林的性能表现最好；
多层感知机模型
class EEGClassifier(nn.Module):def __init__(self, input_dim, num_classes, hidden_dim=256):super(EEGClassifier, self).__init__()self.fc1 = nn.Linear(input_dim, hidden_dim)self.relu = nn.ReLU()self.fc2 = nn.Linear(hidden_dim, num_classes)def forward(self, x):x = self.fc1(x)x = self.relu(x)x = self.fc2(x)return xinput_dim = 2548 # Number of features in EEG signal
num_classes = 3 # Number of classes for classification
model = EEGClassifier(input_dim, num_classes)
loss = nn.CrossEntropyLoss()
CNN+transfomer模型
class EEGConformer(nn.Module):def __init__(self, input_dim, num_classes):super(EEGConformer, self).__init__()# CNNself.conv1 = nn.Conv2d(1, 40, kernel_size=(1, 25), stride=(1, 1))self.conv2 = nn.Conv2d(40, 40, kernel_size=(1, input_dim), stride=(1, 1))self.batchnorm = nn.BatchNorm2d(40)# Transformerself.layernorm1 = nn.LayerNorm(40)self.multiheadattention = nn.MultiheadAttention(40, 1)self.layernorm2 = nn.LayerNorm(40)self.feedworward_block = nn.Sequential(nn.Linear(40, 32),nn.GELU(),nn.Dropout(p=0.1),nn.Linear(32, 40))# MLPself.fc1 = nn.Linear(40, 32)self.fc2 = nn.Linear(32, 32)self.fc3 = nn.Linear(32, num_classes)def forward(self, x):# CNNx = x.unsqueeze(1).unsqueeze(1)x = self.conv1(x)x = self.conv2(x)x = self.batchnorm(x)# Transformerx = x.squeeze()x = self.layernorm1(x)attn_out = self.multiheadattention(x, x, x)x = x + nn.Dropout(0.1)(attn_out[0])x = self.layernorm2(x)x = self.feedworward_block(x)x = nn.Dropout(p=0.1)(x)# MLPx = self.fc1(x)x = F.elu(x)x = nn.Dropout(p=0.5)(x)x = self.fc2(x)x = F.elu(x)x = nn.Dropout(p=0.3)(x)x = self.fc3(x)return xinput_dim = 2524 # Number of features in EEG signal
num_classes = 3 # Number of classes for classification
model = EEGConformer(input_dim, num_classes)
loss = nn.CrossEntropyLoss()代码获取
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