运动想象特征提取与分类MATLAB

function CSPMatrix = learnCSP(EEGSignals,classLabels) % %Input: %EEGSignals: the training EEG signals, composed of 2 classes. These signals %are a structure such that: % EEGSignals.x: the EEG signals as a [Ns * Nc * Nt] Matrix where % Ns: number of EEG samples per trial % Nc: number of channels (EEG electrodes) % nT: number of trials % EEGSignals.y: a [1 * Nt] vector containing the class labels for each trial % EEGSignals.s: the sampling frequency (in Hz) % %Output: %CSPMatrix: the learnt CSP filters (a [Nc*Nc] matrix with the filters as rows) % %See also: extractCSPFeatures %check and initializations nbChannels = size(EEGSignals.x,2); % 通道 nbTrials = size(EEGSignals.x,3); % 实验次数 nbClasses = length(classLabels); % 类别 if nbClasses ~= 2 disp('ERROR! CSP can only be used for two classes'); return; end covMatrices = cell(nbClasses,1); %the covariance matrices for each class %% Computing the normalized covariance matrices for each trial %% 为每个试验计算标准化的协方差矩阵。 trialCov = zeros(nbChannels,nbChannels,nbTrials); for t=1:nbTrials E = EEGSignals.x(:,:,t)'; %note the transpose EE = E * E'; trialCov(:,:,t) = EE ./ trace(EE); end clear E; clear EE; %computing the covariance matrix for each class for c=1:nbClasses %EEGSignals.y==classLabels(c) returns the indeces corresponding to the class labels covMatrices{c} = mean(trialCov(:,:,EEGSignals.y == classLabels(c)),3); end %the total covariance matrix covTotal = covMatrices{1} + covMatrices{2}; %whitening transform of total covariance matrix %caution: the eigenvalues are initially in increasing order注意:特征值最初是递增的 [Ut Dt] = eig(covTotal); eigenvalues = diag(Dt); [eigenvalues egIndex] = sort(eigenvalues, 'descend'); Ut = Ut(:,egIndex); P = diag(sqrt(1./eigenvalues)) * Ut'; %transforming covariance matrix of first class using P %用P变换第一类协方差矩阵 transformedCov1 = P * covMatrices{1} * P'; %EVD of the transformed covariance matrix 变换协方差矩阵的EVD [U1 D1] = eig(transformedCov1); eigenvalues = diag(D1); [eigenvalues egIndex] = sort(eigenvalues, 'descend'); U1 = U1(:, egIndex); CSPMatrix = U1' * P;