往期文章
希望了解更多的道友点这里
0. 分享【脑机接口 + 人工智能】的学习之路
1.1 . 脑电EEG代码开源分享 【1.前置准备-静息态篇】
1.2 . 脑电EEG代码开源分享 【1.前置准备-任务态篇】
2.1 . 脑电EEG代码开源分享 【2.预处理-静息态篇】
2.2 . 脑电EEG代码开源分享 【2.预处理-任务态篇】
3.1 . 脑电EEG代码开源分享 【3.可视化分析-静息态篇】
3.2 . 脑电EEG代码开源分享 【3.可视化分析-任务态篇】
4.1 . 脑电EEG代码开源分享 【4.特征提取-时域篇】
4.2 . 脑电EEG代码开源分享 【4.特征提取-频域篇】
4.3 . 脑电EEG代码开源分享 【4.特征提取-时频域篇】
4.4 . 脑电EEG代码开源分享 【4.特征提取-空域篇】
5 . 脑电EEG代码开源分享 【5.特征选择】
6.1 . 脑电EEG代码开源分享 【6.分类模型-机器学习篇】
6.2 . 脑电EEG代码开源分享 【6.分类模型-深度学习篇】
汇总. 专栏：脑电EEG代码开源分享【文档+代码+经验】
0 . 【深度学习】常用网络总结
脑电EEG代码开源分享 【4.特征提取-时域篇】
一、前言
本文档旨在归纳BCI-EEG-matlab的数据处理代码，作为EEG数据处理的总结，方便快速搭建处理框架的Baseline，实现自动化、模块插拔化、快速化。本文以任务态（锁时刺激，如快速序列视觉呈现）为例，分享脑电EEG的分析处理方法。
脑电数据分析系列。分为以下6个模块：
- 前置准备
- 数据预处理
- 数据可视化
- 特征提取（特征候选集）
- 特征选择（量化特征择优）
- 分类模型
本文内容：【4. 特征提取-频域篇】
提示：以下为各功能代码详细介绍，若节约阅读时间，请下滑至文末的整合代码
二、特征提取 框架介绍
特征提取作为承上启下的重要阶段，是本系列中篇幅最长的部分。承上，紧接预处理结果和可视化分析，对庞大的原始数据进行凝练，用少量维度指标表征整体数据特点；启下，这些代表性、凝练性的特征指标量化了数据性能，为后续的认知解码、状态监测、神经调控等现实需求提供参考。
特征提取的常用特征域为时域、频域、时频域、空域等。本文特征主要为手动设置的经验特征，大多源于脑科学及认知科学的机制结论，提取的特征具有可解释的解剖、认知、物理含义；也有部分是工程人员的实践发现，在模型性能提升中效果显著。
特征提取的代码框图、流程如下所示：
时域-特征提取的主要功能，分为以下2部分：
- 统计特征：过零率、标准差、能量、差分、AR等
- 熵类特征：近似熵、样本熵、李雅普诺夫指数、混合熵等
统计特征是脑电数据最初的刻画方式，前人认为脑电信号属于信号分析的一种，便引入常用的统计学指标来刻画。统计学指标大家比较熟悉，需要对一定时间窗内的数据统计分析，因此对于长时间的脑电信号更友好。统计学指标大多具有现实含义，如过零率代表信号沿零值翻转频率，标准差表示数据偏离均值程度，差分特征表示离散点间的变化速率等。
近期文献中越来越多使用高阶数特征，例如高阶中心距、高阶远点距等，文献中解释说高阶特征包含丰富脑电信息。本人也应用过高阶特征在分类任务中，确实会提升分类性能，但是存在比较严重的过拟合问题，由于高阶特征的高幂特性导致数值指数级增长 or 降低，无论是否后续归一化都会影响特征分布，建议在开集测试分类中慎用。并且其对应的认知含义还不清，仍期待更多的研究进展。
时域-统计学特征：
时域-熵类特征：
熵类特征在近期成为脑电处理的热门话题，文献中声明的熵类特征均有不错性能，例如近似熵、样本熵、李雅普诺夫指数、混合熵等，本人在研究中也发现部分熵类确实提高准确率，推荐大家尝试。
熵类特征的主要思想在于其非线性，通过e或者log计算获得非线性表现。个人根据e或者log曲线认为，熵类特征通过 拉伸 或 压缩 原特征数值，对原特征产生畸变效果，主要决定因素还是原特征的取值范围。熵类特征依靠经验较难，更推荐大家广撒网的尝试，还要注意是否特征进行归一化和标准化。熵类特征也存在短板，例如计算时间明显长于线形特征，并且熵类特征具体含义细节也需要进一步研究。
三、代码格式说明
本文非锁时任务态（下文以静息态代替）范例为：ADHD患者、正常人群在静息状态下的脑模式分类
- 代码名称：代码命名为Festure_ candidate_xxx (time / freq/ imf/ space)
- 参数设置：预处理结果\采样率\时域是否非线性熵特征（耗时）\频域均分分辨度\imf阶数\space对比通道数及频带范围。
- 输入格式：输入格式承接规范预处理最后一项输出，Proprocess_xxx(预处理最终步骤)_target/nontarget。
- 输出及保存格式：输出格式为试次数*特征个数，按照除空域特征外，按照通道的特征拼接，先为1通道内的所有特征，接着2通道的所有特征。保存文件名称为Festure_candidate_xxx(特征域名称)_target/nontarget。
三、脑电特征提取 代码
提示：代码环境为 matlab 2018
3.0 参数设置
可视化内容可以选择，把希望可视化特征域写在Featute_content 中
- 一次进行10人次的批处理，subject_num = [1;10]
- 特征提取内容： Featute_content = [‘time’,‘freq’,‘time_freq’,‘space’]; 时域、频域、时频域、空域均分析
- 时域特征内容：过零率，标准差、近似熵，样本熵，AR。Featute_time_content = [‘cross_zero’,‘std’,‘apen’,‘sampentropy’,‘ar’];
%%0.特征候选集-参数设置
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
data_path = 'C:\Users\EEG\Desktop\basetest_flod\save_fold\';
svae_path = 'C:\Users\EEG\Desktop\basetest_flod\save_fold\';
subject_num =[1;10];% Featute_content =['time\','freq\','time_freq\','space'];
Featute_content =['time\','freq\','time_freq\'];% Featute_time_content =['cross_zero\','std\','apen\','sampentropy\','ar\'];
Featute_time_content =['cross_zero\','std\','ar\'];disp(['||特征候选集-参数设置||']);disp(['特征域内容: ', Featute_content]);disp(['时域-候选集: ', Featute_time_content]);
3.1 标准输入赋值
导入上一步预处理阶段处理后的数据：
%%1.标准输入赋值
Proprocess_target_file =load([data_path ,'Proprocess_target_',num2str(subject_num(1,1)),'_',num2str(subject_num(2,1))]);
Proprocess_nontarget_file =load([data_path ,'Proprocess_nontarget_',num2str(subject_num(1,1)),'_',num2str(subject_num(2,1))]);
stuct_target_name ='Proprocess_target';
stuct_nontarget_name ='Proprocess_nontarget';
Proprocess_target_data = Proprocess_target_file.(stuct_target_name).data;
Proprocess_nontarget_data = Proprocess_nontarget_file.(stuct_nontarget_name).data;
subject_num = Proprocess_target_file.(stuct_target_name).subject_num;
fs_down = Proprocess_target_file.(stuct_target_name).fs_down;
remain_trial_target = Proprocess_target_file.(stuct_target_name).remain_trial;
remain_trial_nontarget = Proprocess_nontarget_file.(stuct_nontarget_name).remain_trial;disp(['目标试次剩余: ',num2str(remain_trial_target),'||平均： ',num2str(mean(remain_trial_target))]);disp(['非目标试次剩余: ',num2str(remain_trial_nontarget),'||平均： ',num2str(mean(remain_trial_nontarget))]);
3.2 时域-特征提取
主函数中 调用时域提取函数
主体调用函数Festure_candidate_time
%%2.时域特征候选集
ifcontains(Featute_content,'time')disp(['时域特征计算中...']);
tic;[Festure_time_target,Festure_time_candidate_num_target]=Festure_candidate_time(Proprocess_target_data,Featute_time_content,remain_trial_target);[Festure_time_nontarget,Festure_time_candidate_num_nontarget]=Festure_candidate_time(Proprocess_nontarget_data,Featute_time_content,remain_trial_nontarget);
t_time_candidate_cost = toc;
Festure_candidate_time_target =[];
Festure_candidate_time_target.data = Festure_time_target;
Festure_candidate_time_target.Featute_time_content = Featute_time_content;
Festure_candidate_time_target.remain_trial_target = remain_trial_target;
Festure_candidate_time_target.Festure_time_candidate_num_target = Festure_time_candidate_num_target;
Festure_candidate_time_target.fs_down = fs_down;
Festure_candidate_time_nontarget =[];
Festure_candidate_time_nontarget.data = Festure_time_nontarget;
Festure_candidate_time_nontarget.Featute_time_content = Featute_time_content;
Festure_candidate_time_nontarget.remain_trial_nontarget = remain_trial_nontarget;
Festure_candidate_time_nontarget.Festure_time_candidate_num_nontarget = Festure_time_candidate_num_nontarget;
Festure_candidate_time_nontarget.fs_down = fs_down;disp(['时域特征计算完毕，耗时： ',num2str(t_time_candidate_cost)]);disp(['时域特征保存中...']);save([ svae_path ,'Festure_candidate_time_target_',num2str(subject_num(1,1)),'_',num2str(subject_num(2,1))],'Festure_candidate_time_target');save([ svae_path , 'Festure_candidate_time_nontarget_',num2str(subject_num(1,1)),'_',num2str(subject_num(2,1))],'Festure_candidate_time_nontarget');disp(['时域特征保存完毕']);
end
3.2.1 过零率 特征
function [Festure_time,Festure_time_candidate_num]=Festure_candidate_time(Standard_input_data,Featute_time_content,remain_trial)%%1.cross_zero
cross_zero =[];ifcontains(Featute_time_content,'cross_zero')
cross_zero =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
cross_zero_channel_temp =zeros(1,size(Standard_input_data{1,1},1));for channel_loop =1:size(Standard_input_data{1,1},1)for point_loop =1:size(Standard_input_data{1,1},2)-1if Standard_input_data{trial_loop,sub_loop}(channel_loop,point_loop)* Standard_input_data{trial_loop,sub_loop}(channel_loop,point_loop+1)<0cross_zero_channel_temp(1,channel_loop)=cross_zero_channel_temp(1,channel_loop)+1;
end
end
end
cross_zero(count_trial,:)= cross_zero_channel_temp;
count_trial = count_trial+1;
end
end
end
3.2.2 标准差 特征
%%2.std
fest_std =[];ifcontains(Featute_time_content,'std')
fest_std =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
std_temp =[];
std_temp =std(Standard_input_data{trial_loop,sub_loop}');fest_std(count_trial,:)= std_temp;
count_trial = count_trial+1;
end
end
end
3.2.3 近似熵 特征（计算时间较长）
%%3.近似熵
fest_apen =[];ifcontains(Featute_time_content,'apen')
r_apen =0.2*fest_std';
fest_apen_2 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
fest_apen_3 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)for channel_loop =1:size(Standard_input_data{1,1},1)fest_apen_2(count_trial,channel_loop)=ApEn(2,r_apen(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);fest_apen_3(count_trial,channel_loop)=ApEn(3,r_apen(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);
end
count_trial = count_trial+1;
end
end
fest_apen =[fest_apen_2 fest_apen_3];
end
近似熵函数：
function apen =ApEn( dim, r, data, tau )%ApEn
% dim : embedded dimension
% r :tolerance(typically 0.2* std)% data : time-series data
% tau : delay time for downsampling
% Changes in version 1% Ver 0 had a minor error in the final step of calculating ApEn
% because it took logarithm after summation of phi's.% In Ver 1, I restored the definition according to original paper's
% definition, to be consistent with most of the work in the
% literature. Note that this definition won't work for Sample
% Entropy which doesn't count self-matching case, because the count
% can be zero and logarithm can fail.%% A new parameter tau is added in the input argument list, so the users
% can apply ApEn on downsampled data by skipping by tau.%---------------------------------------------------------------------% coded by Kijoon Lee, [email protected]
% Ver 0: Aug 4th,2011% Ver 1: Mar 21st,2012%---------------------------------------------------------------------if nargin <4, tau =1; end
if tau >1, data =downsample(data, tau); end
N =length(data);
result =zeros(1,2);for j =1:2
m = dim+j-1;
phi =zeros(1,N-m+1);
dataMat =zeros(m,N-m+1);% setting up data matrix
for i =1:m
dataMat(i,:)=data(i:N-m+i);
end
% counting similar patterns using distance calculation
for i =1:N-m+1
tempMat =abs(dataMat -repmat(dataMat(:,i),1,N-m+1));
boolMat =any((tempMat > r),1);phi(i)=sum(~boolMat)/(N-m+1);
end
% summing over the counts
result(j)=sum(log(phi))/(N-m+1);
end
apen =result(1)-result(2);
end
3.2.4 样本熵 特征（计算时间较长）
%%4.样本熵
fest_sampentropy =[];ifcontains(Featute_time_content,'sampentropy')
r_sampentropy =0.2*fest_std';
fest_sampentropy_2 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
fest_sampentropy_3 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)for channel_loop =1:size(Standard_input_data{1,1},1)fest_sampentropy_2(count_trial,channel_loop)=SampEntropy(2,r_sampentropy(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);fest_sampentropy_3(count_trial,channel_loop)=SampEntropy(3,r_sampentropy(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);
end
count_trial = count_trial+1;
end
end
fest_sampentropy =[fest_sampentropy_2 fest_sampentropy_3];
end
样本熵函数：
function saen =SampEntropy( dim, r, data, tau )% SAMPEN Sample Entropy
% calculates the sample entropy of a given time series data
% SampEn is conceptually similar to approximate entropy(ApEn), but has
% following differences:%1) SampEn does not count self-matching. The possible trouble of
% having log(0) is avoided by taking logarithm at the latest step.%2) SampEn does not depend on the datasize as much as ApEn does. The
% comparison is shown in the graph that is uploaded.% dim : embedded dimension
% r :tolerance(typically 0.2* std)% data : time-series data
% tau : delay time fordownsampling(user can omit this, in which case% the default value is 1)%if nargin <4, tau =1; end
if tau >1, data =downsample(data, tau); end
N =length(data);
result =zeros(1,2);for m = dim:dim+1
Bi =zeros(1,N-m+1);
dataMat =zeros(m,N-m+1);% setting up data matrix
for i =1:m
dataMat(i,:)=data(i:N-m+i);
end
% counting similar patterns using distance calculation
for j =1:N-m+1% calculate Chebyshev distance, excluding self-matching case
dist =max(abs(dataMat -repmat(dataMat(:,j),1,N-m+1)));% calculate Heaviside function of the distance
% User can change it to any other function
%for modified sample entropy(mSampEn) calculation
D =(dist <= r);% excluding self-matching caseBi(j)=(sum(D)-1)/(N-m);
end
% summing over the counts
result(m-dim+1)=sum(Bi)/(N-m+1);
end
saen =-log(result(2)/result(1));
end
3.2.5 AR 特征
%%5.AR
fest_ar =[];ifcontains(Featute_time_content,'ar')
ar_order =8;
fest_ar =zeros(sum(remain_trial),size(Standard_input_data{1,1},1)*ar_order);
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
ar_temp =[];
ar_temp =aryule(Standard_input_data{trial_loop,sub_loop}',ar_order);
ar_temp =ar_temp(:,2:ar_order+1);fest_ar(count_trial,:)=reshape(ar_temp',1,size(Standard_input_data{1,1},1)*ar_order);
count_trial = count_trial+1;
end
end
end
四、时域 特征提取 - 主体函数代码
时域特征主体函数 Festure_candidate_time ：
function [Festure_time,Festure_time_candidate_num]=Festure_candidate_time(Standard_input_data,Featute_time_content,remain_trial)
Festure_time =[];%%1.cross_zero
cross_zero =[];ifcontains(Featute_time_content,'cross_zero')
cross_zero =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
cross_zero_channel_temp =zeros(1,size(Standard_input_data{1,1},1));for channel_loop =1:size(Standard_input_data{1,1},1)for point_loop =1:size(Standard_input_data{1,1},2)-1if Standard_input_data{trial_loop,sub_loop}(channel_loop,point_loop)* Standard_input_data{trial_loop,sub_loop}(channel_loop,point_loop+1)<0cross_zero_channel_temp(1,channel_loop)=cross_zero_channel_temp(1,channel_loop)+1;
end
end
end
cross_zero(count_trial,:)= cross_zero_channel_temp;
count_trial = count_trial+1;
end
end
end
%%2.std
fest_std =[];ifcontains(Featute_time_content,'std')
fest_std =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
std_temp =[];
std_temp =std(Standard_input_data{trial_loop,sub_loop}');fest_std(count_trial,:)= std_temp;
count_trial = count_trial+1;
end
end
end
%%3.近似熵
fest_apen =[];ifcontains(Featute_time_content,'apen')
r_apen =0.2*fest_std';
fest_apen_2 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
fest_apen_3 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)for channel_loop =1:size(Standard_input_data{1,1},1)fest_apen_2(count_trial,channel_loop)=ApEn(2,r_apen(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);fest_apen_3(count_trial,channel_loop)=ApEn(3,r_apen(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);
end
count_trial = count_trial+1;
end
end
fest_apen =[fest_apen_2 fest_apen_3];
end
%%4.样本熵
fest_sampentropy =[];ifcontains(Featute_time_content,'sampentropy')
r_sampentropy =0.2*fest_std';
fest_sampentropy_2 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
fest_sampentropy_3 =zeros(sum(remain_trial),size(Standard_input_data{1,1},1));
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)for channel_loop =1:size(Standard_input_data{1,1},1)fest_sampentropy_2(count_trial,channel_loop)=SampEntropy(2,r_sampentropy(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);fest_sampentropy_3(count_trial,channel_loop)=SampEntropy(3,r_sampentropy(1,count_trial), Standard_input_data{trial_loop,sub_loop}(channel_loop,:),1);
end
count_trial = count_trial+1;
end
end
fest_sampentropy =[fest_sampentropy_2 fest_sampentropy_3];
end
%%5.AR
fest_ar =[];ifcontains(Featute_time_content,'ar')
ar_order =8;
fest_ar =zeros(sum(remain_trial),size(Standard_input_data{1,1},1)*ar_order);
count_trial =1;for sub_loop =1:size(remain_trial,2)for trial_loop =1:remain_trial(1,sub_loop)
ar_temp =[];
ar_temp =aryule(Standard_input_data{trial_loop,sub_loop}',ar_order);
ar_temp =ar_temp(:,2:ar_order+1);fest_ar(count_trial,:)=reshape(ar_temp',1,size(Standard_input_data{1,1},1)*ar_order);
count_trial = count_trial+1;
end
end
end
%% 时域特征合并
Festure_time =[cross_zero fest_std fest_apen fest_sampentropy fest_ar];
Festure_time_candidate_num =[size(cross_zero,2)size(fest_std,2)size(fest_apen,2)size(fest_sampentropy,2)size(fest_ar,2)];
end
总结
脑电在时间分辨率的优势，注定其在时域有丰富的潜在特征。
脑电时域特征也从有严谨推导的统计特征，逐步扩展至实用有效地熵类特征。
推荐大家广泛学习时序信号处理的方法，可以移植和创新大量的新算法。
脑电信号作为信号处理的一种，例如阵列信号处理的经典算法都有应用基础。
同时，对经典特征的融合、组合也是发掘更优混合特征的常用方式。
大家可以探索和发掘是用自己研究的优质特征策略。
目前多样性的特征还在不断发展、丰富，新的特征提取方法逐渐多元化。
进阶特征如脑网络、拓扑图等，基于人工智能的端到端特征提取方法，会在新的专栏中介绍。
囿于能力，挂一漏万，如有笔误请大家指正~
感谢您耐心的观看，本系列更新了约30000字，约3000行开源代码，体量相当于一篇硕士工作。
往期内容放在了文章开头，麻烦帮忙点点赞，分享给有需要的朋友~
坚定初心，本博客永远：
免费拿走，全部开源，全部无偿分享~
To：新想法、鬼点子的道友：
自己：脑机接口+人工智领域，主攻大脑模式解码、身份认证、仿脑模型…
在读博士第3年，在最后1年，希望将代码、文档、经验、掉坑的经历分享给大家~
做的不好请大佬们多批评、多指导~ 虚心向大伙请教！
想一起做些事情 or 奇奇怪怪点子 or 单纯批评我的，请至Rongkaizhang_bci@163.com
本文转载自: https://blog.csdn.net/weixin_44425788/article/details/127028558
版权归原作者 Coco恺撒 所有， 如有侵权，请联系我们删除。
版权归原作者 Coco恺撒 所有， 如有侵权，请联系我们删除。