提供一些可能的代码

好的，以下是可能用到的MATLAB代码，仅供参考：

加载数据：

data = struct();
for i = 1:12
    filename = sprintf('sub%d.mat', i);
    s = load(filename);
    data(i).number = s.number;
end

特征提取：

for i = 1:12
    for j = 1:90
        for k = 1:5
            feature = data(i).number(j, k);
            % 对每个特征进行处理，例如计算平均值、方差等
            % 将处理后的特征存储在新的矩阵feature_matrix中
            feature_matrix(j, k) = processed_feature;
        end
    end
    data(i).features = feature_matrix;
end

训练和测试分类器：

% 将前23个目标试次和非目标试次作为训练数据，后22个目标和非目标试次作为测试数据
train_data = [data(1:12).features(1:23, :); data(1:12).features(45:67, :)];
test_data = [data(1:12).features(24:44, :); data(1:12).features(68:90, :)];

% 构建分类器
svm_model = fitcsvm(train_data, labels);
lda_model = fitcdiscr(train_data, labels);
lr_model = fitclinear(train_data, labels);
bayes_model = fitcnb(train_data, labels);
nn_model = fitcnet(train_data, labels);
knn_model = fitcknn(train_data, labels);
tree_model = fitctree(train_data, labels);

% 进行分类预测
svm_pred = predict(svm_model, test_data);
lda_pred = predict(lda_model, test_data);
lr_pred = predict(lr_model, test_data);
bayes_pred = predict(bayes_model, test_data);
nn_pred = predict(nn_model, test_data);
knn_pred = predict(knn_model, test_data);
tree_pred = predict(tree_model, test_data);

计算评价指标：

% 计算AUC
[svm_x, svm_y, ~, svm_auc] = perfcurve(labels, svm_scores, 1);
[lda_x, lda_y, ~, lda_auc] = perfcurve(labels, lda_scores, 1);
[lr_x, lr_y, ~, lr_auc] = perfcurve(labels, lr_scores, 1);
[bayes_x, bayes_y, ~, bayes_auc] = perfcurve(labels, bayes_scores, 1);
[nn_x, nn_y, ~, nn_auc] = perfcurve(labels, nn_scores, 1);
[knn_x, knn_y, ~, knn_auc] = perfcurve(labels, knn_scores, 1);
[tree_x, tree_y, ~, tree_auc] = perfcurve(labels, tree_scores, 1);

% 计算准确率ACC和混淆矩阵
svm_acc = sum(svm_pred == labels) / length(labels);
lda_acc = sum(lda_pred == labels) / length(labels);
lr_acc = sum(lr_pred == labels) / length(labels);
bayes_acc = sum(bayes_pred == labels) / length(labels);
nn_acc = sum(nn_pred == labels) / length(labels);
knn_acc = sum(knn_pred == labels) / length(labels);
tree_acc = sum(tree_pred == labels) / length(labels);

svm_cm = confusionmat(labels, svm_pred);
lda_cm = confusionmat(labels, lda_pred);
lr_cm = confusionmat(labels, lr_pred);
bayes_cm = confusionmat(labels, bayes_pred);
nn_cm = confusionmat(labels, nn_pred);
knn_cm = confusionmat(labels, knn_pred);
tree_cm = confusionmat(labels, tree_pred);

统计分析：

% 计算均值和方差
auc_mean = [mean(svm_auc), mean(lda_auc), mean(lr_auc), mean(bayes_auc), mean(nn_auc), mean(knn_auc), mean(tree_auc)];
auc_std = [std(svm_auc), std(lda_auc), std(lr_auc), std(bayes_auc), std(nn_auc), std(knn_auc), std(tree_auc)];
acc_mean = [mean(svm_acc), mean(lda_acc), mean(lr_acc), mean(bayes_acc), mean(nn_acc), mean(knn_acc), mean(tree_acc)];
acc_std = [std(svm_acc), std(lda_acc), std(lr_acc), std(bayes_acc), std(nn_acc), std(knn_acc), std(tree_acc)];

% 进行统计分析，例如配对T检验或重复测量的方差分析
[h, p, ci, stats] = ttest(svm_acc, lda_acc);
[p, table, stats] = anova1([svm_acc, lda_acc, lr_acc, bayes_acc, nn_acc, knn_acc, tree_acc]);

这些代码只是一个简单的示例，具体的实现可能会因为数据格式、分类器选择等因素而有所差异，需要根据具体情况进行调整。