eCCA SSVEP matlab代码

以下是一个基于CCA的SSVEP分类器的Matlab代码示例：

%% 加载数据
load('SSVEP_Data.mat');

%% 参数设置
fs = 256; % 采样频率为256Hz
N = size(train_data, 2); % 训练数据的时间点数
T = 1/fs; % 采样时间间隔
t = (0:N-1)*T; % 时间轴数组
frequencies = [8, 10, 12, 15, 20]; % 五个SSVEP刺激频率

%% CCA特征提取和分类
for i = 1:length(frequencies)
    % 生成复指数正弦信号作为参考信号
    reference_signal = exp(1j*2*pi*frequencies(i)*t);
    
    % 对训练数据和测试数据进行CCA特征提取
    [train_features, test_features] = get_cca_features(train_data, test_data, reference_signal);
    
    % 线性判别分析分类
    train_labels = repmat(labels, 1, size(train_features, 2)/length(labels))';
    test_labels = repmat(labels, 1, size(test_features, 2)/length(labels))';
    [predicted_labels, accuracy] = lda_classification(train_features, train_labels, test_features, test_labels);

    % 输出分类结果
    fprintf('刺激频率为 %d Hz 时的分类准确率为 %0.2f%%\n', frequencies(i), accuracy*100);
end

%% 函数定义
function [train_features, test_features] = get_cca_features(train_data, test_data, reference_signal)
    % 将参考信号与数据进行CCA特征提取
    [train_features, test_features] = deal(zeros(size(train_data, 1), size(train_data, 3)*2));
    for j = 1:size(train_data, 1)
        [A, B, ~, ~, ~, D] = canoncorr(squeeze(train_data(j,:,:)).', reference_signal.');
        train_features(j,:) = [D(:,1)'*A'; D(:,1)'*B'];
        test_features(j,:) = [squeeze(test_data(j,:,:))*A(:,1); squeeze(test_data(j,:,:))*B(:,1)];
    end
end

function [predicted_labels, accuracy] = lda_classification(train_features, train_labels, test_features, test_labels)
    % 线性判别分析分类
    [coeff, score, ~, ~, explained] = pca(train_features);
    n_components = find(cumsum(explained)/sum(explained) >= 0.99, 1);
    train_features_pca = train_features*coeff(:,1:n_components);
    test_features_pca = test_features*coeff(:,1:n_components);
    model = fitcdiscr(train_features_pca, train_labels);
    predicted_labels = predict(model, test_features_pca);
    accuracy = sum(predicted_labels == test_labels)/length(test_labels);
end

其中，`train_data`和`test_data`是大小为(N_channels, N_samples, N_trials)的三维数组，分别表示训练数据和测试数据。`labels`是大小为1×N_trials的标签数组。`get_cca_features`函数实现了CCA特征提取，`lda_classification`函数实现了线性判别分析分类。在主函数中，首先设置参数，然后对每个SSVEP刺激频率，生成参考信号并进行CCA特征提取和线性判别分析分类。最后输出分类结果。