 We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects. For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
<meaning-changed>  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects. For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
 We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% for session-by-session alignment, FG-W resulted in alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.