udpate

2020MAR30/notes.md

@@ -7,12 +7,15 @@
  * 100 components
 ![](figs/basis_comparison_boxplot.png)
  * 50 components
+![](figs/basis_comparison_boxplot50.png)
 * automatically find the number of components? Bayesian methods? 
 * For now the basis only used information from resting state data of the subject and other subjects, maybe try to incorprate information from other subjects' task activation information?
 
 ### 2) using residual basis of the self subject and the coefficients of other subjects to make predictions
  * Using residual bases to make prediction differentiates self from others more than using original basis
 <img src="figs/improv.png" width="49%"> <img src="figs/improv_reg.png" width="49%"> 
+ * ...and improves prediction accuracy
+![](figs/model_accuracy.png)
  * How to further improve the performance?
 
 ### 3) Amplitude prediction