共有四个函数
cspfunc:针对两类脑电和标签提取csp特征并获取滤波器
cspfilt:使用获取的csp滤波器滤波
fbcspfunc:针对两类脑电和标签提取fbcsp特征并获取滤波器(fbcsp 就是分频段组合的 csp,可以是csps?)
fbcspfilt:使用获取的fbcsp滤波器滤波
需要注意的是,必须先进行 func 计算获取滤波器参数,才能进行 filt
cspfunc 函数(主要的函数,借鉴上边博主的demo,大家如果觉得这篇有用也请一定去支持一下他):
function [cspfeature cspw] = cspfunc(eeg,label,m)
% eeg:分段后的三维脑电(已滤波),采样点 * 通道 * 实验次数
% label:脑电的标签,列向量,值只有 1 和 2
% m:csp 参数
% cspfeature:提取到的fbcsp 特征,主要用于训练,如果是测试,需要用 fbcspfilt 函数滤波
% cspw:fbcsp 滤波器
%check and initializations
EEG_Channels = size(eeg,2); % 通道数目
EEG_Trials = size(eeg,3); % 实验次数
classLabels = unique(label);% Return non-repeating values
EEG_Classes = length(classLabels); % 标签类个数
covMatrix = cell(EEG_Classes,1); % 协方差矩阵
% Computing the normalized covariance matrices for each trial
trialCov = zeros(EEG_Channels,EEG_Channels,EEG_Trials);
for i = 1:EEG_Trials
E = eeg(:,:,i)';
EE = E*E';
trialCov(:,:,i) = EE./trace(EE); % 计算协方差矩阵
end
clear E;
clear EE;
% 计算每一类样本数据的空间协方差之和
for i = 1:EEG_Classes
covMatrix{i} = mean(trialCov(:,:,label == classLabels(i)),3);
end
% 计算两类数据的空间协方差之和
covTotal = covMatrix{1} + covMatrix{2};
% 计算特征向量和特征矩阵
[Uc,Dt] = eig(covTotal);
% 特征值要降序排列
eigenvalues = diag(Dt);
[eigenvalues,egIndex] = sort(eigenvalues, 'descend');% 降序
Ut = Uc(:,egIndex);
% 矩阵白化
P = diag(sqrt(1./eigenvalues))*Ut';
% 矩阵P作用求公共特征向量transformedCov1
transformedCov1 = P*covMatrix{1}*P';
%计算公共特征向量transformedCov1的特征向量和特征矩阵
[U1,D1] = eig(transformedCov1);
eigenvalues = diag(D1);
[eigenvalues,egIndex] = sort(eigenvalues, 'descend');% 降序排列
U1 = U1(:, egIndex);
% 计算投影矩阵W
CSPMatrix = U1' * P;
% 计算特征矩阵
FilterPairs = m; % CSP特征选择参数m CSP特征为2*m个
features_train = zeros(EEG_Trials, 2*FilterPairs+1);
Filter = CSPMatrix([1:FilterPairs (end-FilterPairs+1):end],:);
cspw = Filter;
%extracting the CSP features from each trial
for t=1:EEG_Trials
% projecting the data onto the CSP filters
projectedTrial_train = Filter * eeg(:,:,t)';
% generating the features as the log variance of the projected signals
variances_train = var(projectedTrial_train,0,2);
for f=1:length(variances_train)
features_train(t,f) = log(variances_train(f));
% features_train(t,f) = log(variances_train(f)/sum(variances_train)); %修改后对应公式
end
end
cspfeature = features_train(:,1:2*m);
end
cspfilt函数:
function cspfiltedeeg = cspfilt(eeg,cspw)
%使用获取到的 csp 滤波器滤波,获取方式见 cspfunc
Filter = cspw;
m = size(cspw,1)/2;
features_train = zeros(size(eeg,3), 2*m+1);
%extracting the CSP features from each trial
for t=1:size(eeg,3)
% projecting the data onto the CSP filters
projectedTrial_train = Filter * eeg(:,:,t)';
% generating the features as the log variance of the projected signals
variances_train = var(projectedTrial_train,0,2);
for f=1:length(variances_train)
features_train(t,f) = log(variances_train(f));
% features_train(t,f) = log(variances_train(f)/sum(variances_train)); %修改后对应公式
end
end
cspfiltedeeg = features_train(:,1:2*m);
end
fbcspfunc 函数:
function [fbcspfeature,fbcspw] = fbcspfunc(eeg,label,m,bands,fs,filterorder)
% 调用函数 cspfunc,针对 bands 中的每个频带进行 csp,然后拼接特征
% eeg:分段后的三维脑电(已滤波),采样点 * 通道 * 实验次数
% label:脑电的标签,列向量,值只有 1 和 2
% m:csp 参数
% bands:不同的频带,如[1 4;4 7;8 13]
% fs:采样率
% filterorder:滤波器阶数
% fbcspfeature:提取到的fbcsp 特征,主要用于训练,如果是测试,需要用 fbcspfilt 函数滤波
% fbcspw:fbcsp 滤波器
fbcspfeature = [];
fbcspw=[];
for i = 1:size(bands,1)
udfilter=designfilt('bandpassiir','FilterOrder',filterorder, ...
'HalfPowerFrequency1',bands(i,1),'HalfPowerFrequency2',bands(i,2), ...
'SampleRate',fs);
filtedeeg = zeros(size(eeg));
for trial = 1:size(eeg,3)
for ch = 1:size(eeg,2)
filtedeeg(:,ch,trial)=filter(udfilter,eeg(:,ch,trial));
end
end
[cspfeature cspw] = cspfunc(filtedeeg,label,m);
fbcspfeature = cat(2,fbcspfeature,cspfeature);
fbcspw = cat(3,fbcspw,cspw);
end
end
fbcspfilt 函数:
function fbcspfiltedeeg = fbcspfilt(eeg,fbcspw,bands,fs,filterorder)
%使用获取到的 csp 滤波器滤波,获取方式见 fbcspfunc
Filter = fbcspw;
m = size(fbcspw,1)/2;
fbcspfiltedeeg = [];
for i=1:size(fbcspw,3)
udfilter=designfilt('bandpassiir','FilterOrder',filterorder, ...
'HalfPowerFrequency1',bands(i,1),'HalfPowerFrequency2',bands(i,2), ...
'SampleRate',fs);
filtedeeg = zeros(size(eeg));
for trial = 1:size(eeg,3)
for ch = 1:size(eeg,2)
filtedeeg(:,ch,trial)=filter(udfilter,eeg(:,ch,trial));
end
end
features_train = zeros(size(filtedeeg,3), 2*m+1);
%extracting the CSP features from each trial
for t=1:size(filtedeeg,3)
% projecting the data onto the CSP filters
projectedTrial_train = Filter(:,:,i) * filtedeeg(:,:,t)';
% generating the features as the log variance of the projected signals
variances_train = var(projectedTrial_train,0,2);
for f=1:length(variances_train)
features_train(t,f) = log(variances_train(f));
% features_train(t,f) = log(variances_train(f)/sum(variances_train)); %修改后对应公式
end
end
cspfiltedeeg = features_train(:,1:2*m);
fbcspfiltedeeg = cat(2,fbcspfiltedeeg,cspfiltedeeg);
end
end
按照格式使用函数就行
如:
[fbcspfeature fbcspw]= fbcspfunc(eeg,label,2,[1 4;4 7;8 13],512,4);
fbcspfiltedeeg = fbcspfilt(eeg,fbcspw,[1 4;4 7;8 13],512,4);