Download “Cross Wavelet and Wavelet Coherence Toolbox”

14 sec read

The “Cross Wavelet and Wavelet Coherence Toolbox” download link by Grinsted et al (http://www.pol.ac.uk/home/research/waveletcoherence/) is dead. We will send you an active download link upon your request (an email with the download link will be sent to you automatically after you fill the form below):





To learn more about wavelet coherence analysis, see https://www.alivelearn.net/?s=wavelet

第十六期 fNIRS Journal Club 通知 2021/01/23,1pm

瑞典 Karolinska Institutet的潘亚峰博士将为大家讲解他们最近发布的一篇用超扫描研究教师学生关系的文章。热烈欢迎大家参与讨论。潘博士为了这次报告,需要一大早就起床。因此本次报告的时间比过去要稍晚一点。 时间: 北京时间2021年1月23日周六下午1点地点: https://zoom.com房间号: 815 4986 9861密码: 796475 Pan, Guyon, Borragán, Hu, Peigneux (2020) Interpersonal brain synchronization with instructor compensates for learner’s...
Xu Cui
53 sec read

第十五期 fNIRS Journal Club 视频

北京时间2020年12月27日周日上午10点, 香港中文大学二年级博士生胡玥讲了一篇用神经网络去除运动伪迹的文章。视频如下: Youtube: https://youtu.be/mZkGzm1R7ak Youku: https://v.youku.com/v_show/id_XNTAyODUyMTEyOA==.html
Xu Cui
4 sec read

第十五期 fNIRS Journal Club 通知 2020/12/27,10am

香港中文大学二年级博士生胡玥为大家介绍一篇方法学文献,即基于人工神经网络重構的多通道fNIRS信号运动伪影校正。该方法不仅对心理学,还对运动科学以及康复医学等领域的研究具有重要的参考价值,热烈欢迎大家参与讨论。 时间: 北京时间2020年12月27日周日上午10点地点: https://zoom.com房间号: 859 4100 6556 密码: 467563 Lee, G., Jin, S. H., & An, J. (2018). Motion artifact correction of multi-measured functional...
Xu Cui
51 sec read

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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