Download “Cross Wavelet and Wavelet Coherence Toolbox”

14 sec read

第三十三期 fNIRS Journal Club 通知 2022/08/27,9am 翟雪彤

Brain AnalyzIR toolbox是一款基于MATLAB开发的高效的fNIRS处理工具箱,由匹兹堡大学Theodore Huppert团队开发(Homer的主要开发者)。来自Theodore
Xu Cui
11 sec read

第三十二期 fNIRS Journal Club 视频 郑一磊

北京航空航天大学的郑一磊博士为大家分享如何利用fNIRS研究人在执行精细运动任务时的脑活动及相关神经机制。 Youtube:https://youtu.be/oCqOXh_-JzE Youku:htt
Xu Cui
11 sec read

第三十二期 fNIRS Journal Club 通知 2022/07/30,10am 郑一磊

智能人机交互系统的研发涉及对人体精细触力觉的理解,如人对任务的控制力度和控制精度。北京航空航天大学的郑一磊博士将为大家分享如何利用fNIRS研究人在执行精细运动任务时的脑活动及相关神经机制,热烈欢迎大
Xu Cui
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2 Replies to “Download “Cross Wavelet and Wavelet Coherence Toolbox””

  1. Dear Prof. Xu,
    I am writing to get more information about SVM classification.

    I have one matrix with the inputs of the training data (X) and matrix with the labels (e.g. Y, my labels are -1, 1). I would like to do cross validation with n-fold (for example n=10) test on my data and I am going to obtain accuracy, Sensitivity and Specificity parameters to determine the performance of classifier.
    I did my classification based on your tutorial (http://www.alivelearn.net/?s=libsvm).

    bestcv = 0;
    for log2c = -1:3,
    for log2g = -4:1,
    cmd = [‘-v 5 -c ‘, num2str(2^log2c), ‘ -g ‘, num2str(2^log2g)];
    cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
    if (cv >= bestcv),
    bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf(‘%g %g %g (best c=%g, g=%g, rate=%g)\n’, log2c, log2g, cv, bestc, bestg, bestcv);
    end
    end
    Based on this code I can obtain cross validation accuracy, but in addition to accuracy I would like to obtain “predicted_label” according to cross validation.
    Would you please guide me how can I obtain final predicted_label after cross validation.

    Many thanks for kindly considering my question.
    I look forward to hearing from you.
    Best,
    Iman

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