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简介
脑机接口（BCI）使用神经活动作为控制信号，实现与计算机的直接通信。这种神经信号通常是从各种研究透彻的脑电图(EEG)信号中挑选出来的。卷积神经网络（CNN）主要用来自动特征提取和分类，其在计算机视觉和语音识别领域中的使用已经很广泛。CNN已成功应用于基于EEG的BCI；但是，CNN主要应用于单个BCI范式，在其他范式中的使用比较少，论文作者提出是否可以设计一个CNN架构来准确分类来自不同BCI范式的EEG信号，同时尽可能地紧凑（定义为模型中的参数数量）。该论文介绍了EEGNet，这是一种用于基于EEG的BCI的紧凑型卷积神经网络。论文介绍了使用深度和可分离卷积来构建特定于EEG的模型，该模型封装了脑机接口中常见的EEG特征提取概念。论文通过四种BCI范式(P300视觉诱发电位、错误相关负性反应(ERN)、运动相关皮层电位(MRCP)和感觉运动节律(SMR))，将EEGNet在主体内和跨主体分类方面与目前最先进的方法进行了比较。结果显示，在训练数据有限的情况下，EEGNet比参考算法具有更强的泛化能力和更高的性能。同时论文也证明了EEGNet可以有效地推广到ERP和基于振荡的BCI。
实验结果如下图，P300数据集的所有CNN模型之间的差异非常小，但是MRCP数据集却存在显著的差异，两个EEGNet模型的性能都优于所有其他模型。对于ERN数据集来说，两个EEGNet模型的性能都优于其他所有模型(p < 0.05)。
EEGNet网络原理
EEGNet网络结构图：
EEGNet原理架构如下：
EEGNet网络实现
import numpy as np
from sklearn.metrics import roc_auc_score, precision_score, recall_score, accuracy_score
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim定义网络模型：
class EEGNet(nn.Module):
def __init__(self):
super(EEGNet, self).__init__()
self.T = 120
# Layer 1
self.conv1 = nn.Conv2d(1, 16, (1, 64), padding = 0)
self.batchnorm1 = nn.BatchNorm2d(16, False)
# Layer 2
self.padding1 = nn.ZeroPad2d((16, 17, 0, 1))
self.conv2 = nn.Conv2d(1, 4, (2, 32))
self.batchnorm2 = nn.BatchNorm2d(4, False)
self.pooling2 = nn.MaxPool2d(2, 4)
# Layer 3
self.padding2 = nn.ZeroPad2d((2, 1, 4, 3))
self.conv3 = nn.Conv2d(4, 4, (8, 4))
self.batchnorm3 = nn.BatchNorm2d(4, False)
self.pooling3 = nn.MaxPool2d((2, 4))
# 全连接层
# 此维度将取决于数据中每个样本的时间戳数。
# I have 120 timepoints.
self.fc1 = nn.Linear(4*2*7, 1)
def forward(self, x):
# Layer 1
x = F.elu(self.conv1(x))
x = self.batchnorm1(x)
x = F.dropout(x, 0.25)
x = x.permute(0, 3, 1, 2)
# Layer 2
x = self.padding1(x)
x = F.elu(self.conv2(x))
x = self.batchnorm2(x)
x = F.dropout(x, 0.25)
x = self.pooling2(x)
# Layer 3
x = self.padding2(x)
x = F.elu(self.conv3(x))
x = self.batchnorm3(x)
x = F.dropout(x, 0.25)
x = self.pooling3(x)
# 全连接层
x = x.view(-1, 4*2*7)
x = F.sigmoid(self.fc1(x))
return x定义评估指标：
acc：准确率
auc:AUC 即 ROC 曲线对应的面积
recall:召回率
precision:精确率
fmeasure：F值
def evaluate(model, X, Y, params = ["acc"]):
results = []
batch_size = 100
predicted = []
for i in range(len(X)//batch_size):
s = i*batch_size
e = i*batch_size+batch_size
inputs = Variable(torch.from_numpy(X[s:e]))
pred = model(inputs)
predicted.append(pred.data.cpu().numpy())
inputs = Variable(torch.from_numpy(X))
predicted = model(inputs)
predicted = predicted.data.cpu().numpy()
"""
设置评估指标：
acc：准确率
auc:AUC 即 ROC 曲线对应的面积
recall:召回率
precision:精确率
fmeasure：F值
"""
for param in params:
if param == 'acc':
results.append(accuracy_score(Y, np.round(predicted)))
if param == "auc":
results.append(roc_auc_score(Y, predicted))
if param == "recall":
results.append(recall_score(Y, np.round(predicted)))
if param == "precision":
results.append(precision_score(Y, np.round(predicted)))
if param == "fmeasure":
precision = precision_score(Y, np.round(predicted))
recall = recall_score(Y, np.round(predicted))
results.append(2*precision*recall/ (precision+recall))
return results构建网络EEGNet，并设置二分类交叉熵和Adam优化器
# 定义网络
net = EEGNet()
# 定义二分类交叉熵 (Binary Cross Entropy)
criterion = nn.BCELoss()
# 定义Adam优化器
optimizer = optim.Adam(net.parameters())创建数据集
"""
生成训练数据集，数据集有100个样本
训练数据X_train:为[0,1)之间的随机数;
标签数据y_train:为0或1
"""
X_train = np.random.rand(100, 1, 120, 64).astype('float32')
y_train = np.round(np.random.rand(100).astype('float32'))
"""
生成验证数据集，数据集有100个样本
验证数据X_val:为[0,1)之间的随机数;
标签数据y_val:为0或1
"""
X_val = np.random.rand(100, 1, 120, 64).astype('float32')
y_val = np.round(np.random.rand(100).astype('float32'))
"""
生成测试数据集，数据集有100个样本
测试数据X_test:为[0,1)之间的随机数;
标签数据y_test:为0或1
"""
X_test = np.random.rand(100, 1, 120, 64).astype('float32')
y_test = np.round(np.random.rand(100).astype('float32'))训练并验证
batch_size = 32
# 训练 循环
for epoch in range(10):
print("\nEpoch ", epoch)
running_loss = 0.0
for i in range(len(X_train)//batch_size-1):
s = i*batch_size
e = i*batch_size+batch_size
inputs = torch.from_numpy(X_train[s:e])
labels = torch.FloatTensor(np.array([y_train[s:e]]).T*1.0)
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
# 验证
params = ["acc", "auc", "fmeasure"]
print(params)
print("Training Loss ", running_loss)
print("Train - ", evaluate(net, X_train, y_train, params))
print("Validation - ", evaluate(net, X_val, y_val, params))
print("Test - ", evaluate(net, X_test, y_test, params))Epoch 0
['acc', 'auc', 'fmeasure']
Training Loss 1.6107637286186218
Train - [0.52, 0.5280448717948718, 0.6470588235294118]
Validation - [0.55, 0.450328407224959, 0.693877551020408]
Test - [0.54, 0.578926282051282, 0.6617647058823529]
Epoch 1
['acc', 'auc', 'fmeasure']
Training Loss 1.5536684393882751
Train - [0.45, 0.41145833333333337, 0.5454545454545454]
Validation - [0.55, 0.4823481116584565, 0.6564885496183207]
Test - [0.65, 0.6530448717948717, 0.7107438016528926]
Epoch 2
['acc', 'auc', 'fmeasure']
Training Loss 1.5197088718414307
Train - [0.49, 0.5524839743589743, 0.5565217391304348]
Validation - [0.53, 0.5870279146141215, 0.5436893203883495]
Test - [0.57, 0.5428685897435898, 0.5567010309278351]
Epoch 3
['acc', 'auc', 'fmeasure']
Training Loss 1.4534167051315308
Train - [0.53, 0.5228365384615385, 0.4597701149425287]
Validation - [0.5, 0.48152709359605916, 0.46808510638297873]
Test - [0.61, 0.6502403846153847, 0.5517241379310345]
Epoch 4
['acc', 'auc', 'fmeasure']
Training Loss 1.3821702003479004
Train - [0.46, 0.4651442307692308, 0.3076923076923077]
Validation - [0.47, 0.5977011494252874, 0.29333333333333333]
Test - [0.52, 0.5268429487179488, 0.35135135135135137]
Epoch 5
['acc', 'auc', 'fmeasure']
Training Loss 1.440490186214447
Train - [0.56, 0.516025641025641, 0.35294117647058826]
Validation - [0.36, 0.3801313628899836, 0.2]
Test - [0.53, 0.6113782051282052, 0.27692307692307694]
Epoch 6
['acc', 'auc', 'fmeasure']
Training Loss 1.4722238183021545
Train - [0.47, 0.4194711538461539, 0.13114754098360656]
Validation - [0.46, 0.5648604269293925, 0.2285714285714286]
Test - [0.5, 0.5348557692307693, 0.10714285714285714]
Epoch 7
['acc', 'auc', 'fmeasure']
Training Loss 1.3460421562194824
Train - [0.51, 0.44871794871794873, 0.1694915254237288]
Validation - [0.44, 0.4490968801313629, 0.2]
Test - [0.53, 0.4803685897435898, 0.14545454545454545]
Epoch 8
['acc', 'auc', 'fmeasure']
Training Loss 1.3336675763130188
Train - [0.54, 0.4130608974358974, 0.20689655172413793]
Validation - [0.39, 0.40394088669950734, 0.14084507042253522]
Test - [0.51, 0.5400641025641025, 0.19672131147540983]
Epoch 9
['acc', 'auc', 'fmeasure']
Training Loss 1.438510239124298
Train - [0.53, 0.5392628205128205, 0.22950819672131148]
Validation - [0.42, 0.4848111658456486, 0.09375]
Test - [0.56, 0.5420673076923076, 0.2413793103448276]
参考
EEGNet: 深度学习应用于脑电信号特征提取
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