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]

			
原文：https://blog.csdn.net/zyb228107/article/details/121882410
		
		

			

	

		
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