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眼睛闭合并打开脑电图数据预测？

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可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan）
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Commented: 可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan）on 24 Jul 2020
I am having a resting state EEG data and want to predict the Eyes closed and open conditions. How can I implement code in matlab and any prototype code or function available to figure out this problem. Thanks!

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Accepted Answer

Jingwei Too
Jingwei Too on 23 Jul 2020
您可以遵循这些步骤
(1) Extract the features from the EEG signals (for both eye closed and open)
（2）您可以使用特征向量和标签使用以下工具箱执行预测
//www.tatmou.com/matlabcentral/fileexchange/71461-simple-machine-learning-algorithms-for-classification?s_tid=prof_contriblnk
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可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan）
可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan） on 23 Jul 2020
亲爱的辛威！
I had successfully applied your toolbox and it's working perfectly. At the frontal channel the accuracies are :
Classification Accuracy (KNN): 72.2546 %
Classification Accuracy (DA): 74.0612 %
Classification Accuracy (NB): 69.5661 %
Classification Accuracy (DT): 71.1095 %
Classification Accuracy (SVM): 70.1351 %
Classification Accuracy (RF): 68.2575 %
However I am bit confused and could you please eloborate these parameters in a bit detail.
(1) Accuracy for each fold in k-fold cross-validation
(2) Average accuracy over k-folds
(3) Confusion matrix.
Thanks once again for developing such an amazing toolbox for the Machine Learning applications.

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更多的Answers (1)

Jingwei Too
Jingwei Too on 23 Jul 2020
Let say you have 10 participants, and 38 channels. You extract one feature (alpha power) from each participant. Then your feature vector will be 10 x 38 (Instances x features). However, if you extract two features from each participant. Then your feature vector will be 10 x 76. Hope this clear.
然后，对于标签，您有10位参与者，假设他们的第5位是闭上眼睛的，最后5只注视着眼睛。然后您的标签将为[1; 1; 1; 1; 1; 1; 0; 0; 0; 0; 0; 0]。“ 1”表示闭上眼睛，“ 0”表示眼睛睁开。“ 1”和“ 0”您可以用任何值更改它们，但只需确保它们与众不同，因为它们代表不同的条件（闭合或打开）。
1 Comment
可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan）
可汗穆罕默德·阿德尔·汗（Khan Muhammad Adeel Khan） on 24 Jul 2020
Thaks精卫!我已经解决d the problem and it works perfectly. You really make ML so simple and hats off to you for this great contribution

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