<p>The rows of the matrix indicate the actual roughness provided to the participants and the columns indicate the predictions by a neural decoder. The cells of highest accuracy in each row are highlighted in red and the frequent confusions, of which the misclassification rates exceeded the chance level (20%), are highlighted in light red.</p
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
<p>The confusion matrix for discriminating A three alternatives: and B nine alternatives: is shown...
<p>The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network...
Confusion matrix using exclusively task fMRI data for the selection of the best classification model...
<p>The matrices show the average locations estimated by the classifier as a function of the actual w...
<p>The matrices show the average locations estimated by the classifier as a function of the actual w...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
<p>(A) Maximum circular correlation between predicted and actual target direction for each sub-band,...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>The decoder was trained and tested on good exemplars (left column) and trained and tested on bad ...
<p><b>A.</b> Classification result over all 21 neurons. Blue stars and red circle denote the classif...
<p>The data presented in each column is the probability of the participants' judgments. The recognit...
<p>Searchlight analysis identified four brain regions showing significant decoding performance in th...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
<p>Panel A: Confusion matrices for identifying one of the four tasks from one day to another for eac...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
<p>The confusion matrix for discriminating A three alternatives: and B nine alternatives: is shown...
<p>The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network...
Confusion matrix using exclusively task fMRI data for the selection of the best classification model...
<p>The matrices show the average locations estimated by the classifier as a function of the actual w...
<p>The matrices show the average locations estimated by the classifier as a function of the actual w...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
<p>(A) Maximum circular correlation between predicted and actual target direction for each sub-band,...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>The decoder was trained and tested on good exemplars (left column) and trained and tested on bad ...
<p><b>A.</b> Classification result over all 21 neurons. Blue stars and red circle denote the classif...
<p>The data presented in each column is the probability of the participants' judgments. The recognit...
<p>Searchlight analysis identified four brain regions showing significant decoding performance in th...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
<p>Panel A: Confusion matrices for identifying one of the four tasks from one day to another for eac...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
<p>The confusion matrix for discriminating A three alternatives: and B nine alternatives: is shown...
<p>The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network...